types of research studies trials

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What types of studies are there.

Created: June 15, 2016 ; Last Update: September 8, 2016 ; Next update: 2020.

There are various types of scientific studies such as experiments and comparative analyses, observational studies, surveys, or interviews. The choice of study type will mainly depend on the research question being asked.

When making decisions, patients and doctors need reliable answers to a number of questions. Depending on the medical condition and patient's personal situation, the following questions may be asked:

• What is the cause of the condition?
• What is the natural course of the disease if left untreated?
• What will change because of the treatment?
• How many other people have the same condition?
• How do other people cope with it?

Each of these questions can best be answered by a different type of study.

In order to get reliable results, a study has to be carefully planned right from the start. One thing that is especially important to consider is which type of study is best suited to the research question. A study protocol should be written and complete documentation of the study's process should also be done. This is vital in order for other scientists to be able to reproduce and check the results afterwards.

The main types of studies are randomized controlled trials (RCTs), cohort studies, case-control studies and qualitative studies.

• Randomized controlled trials

If you want to know how effective a treatment or diagnostic test is, randomized trials provide the most reliable answers. Because the effect of the treatment is often compared with "no treatment" (or a different treatment), they can also show what happens if you opt to not have the treatment or diagnostic test.

When planning this type of study, a research question is stipulated first. This involves deciding what exactly should be tested and in what group of people. In order to be able to reliably assess how effective the treatment is, the following things also need to be determined before the study is started:

• How long the study should last
• How many participants are needed
• How the effect of the treatment should be measured

For instance, a medication used to treat menopause symptoms needs to be tested on a different group of people than a flu medicine. And a study on treatment for a stuffy nose may be much shorter than a study on a drug taken to prevent strokes.

“Randomized” means divided into groups by chance. In RCTs participants are randomly assigned to one of two or more groups. Then one group receives the new drug A, for example, while the other group receives the conventional drug B or a placebo (dummy drug). Things like the appearance and taste of the drug and the placebo should be as similar as possible. Ideally, the assignment to the various groups is done "double blinded," meaning that neither the participants nor their doctors know who is in which group.

The assignment to groups has to be random in order to make sure that only the effects of the medications are compared, and no other factors influence the results. If doctors decided themselves which patients should receive which treatment, they might – for instance – give the more promising drug to patients who have better chances of recovery. This would distort the results. Random allocation ensures that differences between the results of the two groups at the end of the study are actually due to the treatment and not something else.

Randomized controlled trials provide the best results when trying to find out if there is a cause-and-effect relationship. RCTs can answer questions such as these:

• Is the new drug A better than the standard treatment for medical condition X?
• Does regular physical activity speed up recovery after a slipped disk when compared to passive waiting?
• Cohort studies

A cohort is a group of people who are observed frequently over a period of many years – for instance, to determine how often a certain disease occurs. In a cohort study, two (or more) groups that are exposed to different things are compared with each other: For example, one group might smoke while the other doesn't. Or one group may be exposed to a hazardous substance at work, while the comparison group isn't. The researchers then observe how the health of the people in both groups develops over the course of several years, whether they become ill, and how many of them pass away. Cohort studies often include people who are healthy at the start of the study. Cohort studies can have a prospective (forward-looking) design or a retrospective (backward-looking) design. In a prospective study, the result that the researchers are interested in (such as a specific illness) has not yet occurred by the time the study starts. But the outcomes that they want to measure and other possible influential factors can be precisely defined beforehand. In a retrospective study, the result (the illness) has already occurred before the study starts, and the researchers look at the patient's history to find risk factors.

Cohort studies are especially useful if you want to find out how common a medical condition is and which factors increase the risk of developing it. They can answer questions such as:

• How does high blood pressure affect heart health?
• Does smoking increase your risk of lung cancer?

For example, one famous long-term cohort study observed a group of 40,000 British doctors, many of whom smoked. It tracked how many doctors died over the years, and what they died of. The study showed that smoking caused a lot of deaths, and that people who smoked more were more likely to get ill and die.

• Case-control studies

Case-control studies compare people who have a certain medical condition with people who do not have the medical condition, but who are otherwise as similar as possible, for example in terms of their sex and age. Then the two groups are interviewed, or their medical files are analyzed, to find anything that might be risk factors for the disease. So case-control studies are generally retrospective.

Case-control studies are one way to gain knowledge about rare diseases. They are also not as expensive or time-consuming as RCTs or cohort studies. But it is often difficult to tell which people are the most similar to each other and should therefore be compared with each other. Because the researchers usually ask about past events, they are dependent on the participants’ memories. But the people they interview might no longer remember whether they were, for instance, exposed to certain risk factors in the past.

Still, case-control studies can help to investigate the causes of a specific disease, and answer questions like these:

• Do HPV infections increase the risk of cervical cancer?
• Is the risk of sudden infant death syndrome (“cot death”) increased by parents smoking at home?

Cohort studies and case-control studies are types of "observational studies."

• Cross-sectional studies

Many people will be familiar with this kind of study. The classic type of cross-sectional study is the survey: A representative group of people – usually a random sample – are interviewed or examined in order to find out their opinions or facts. Because this data is collected only once, cross-sectional studies are relatively quick and inexpensive. They can provide information on things like the prevalence of a particular disease (how common it is). But they can't tell us anything about the cause of a disease or what the best treatment might be.

Cross-sectional studies can answer questions such as these:

• How tall are German men and women at age 20?
• How many people have cancer screening?
• Qualitative studies

This type of study helps us understand, for instance, what it is like for people to live with a certain disease. Unlike other kinds of research, qualitative research does not rely on numbers and data. Instead, it is based on information collected by talking to people who have a particular medical condition and people close to them. Written documents and observations are used too. The information that is obtained is then analyzed and interpreted using a number of methods.

Qualitative studies can answer questions such as these:

• How do women experience a Cesarean section?
• What aspects of treatment are especially important to men who have prostate cancer?
• How reliable are the different types of studies?

Each type of study has its advantages and disadvantages. It is always important to find out the following: Did the researchers select a study type that will actually allow them to find the answers they are looking for? You can’t use a survey to find out what is causing a particular disease, for instance.

It is really only possible to draw reliable conclusions about cause and effect by using randomized controlled trials. Other types of studies usually only allow us to establish correlations (relationships where it isn’t clear whether one thing is causing the other). For instance, data from a cohort study may show that people who eat more red meat develop bowel cancer more often than people who don't. This might suggest that eating red meat can increase your risk of getting bowel cancer. But people who eat a lot of red meat might also smoke more, drink more alcohol, or tend to be overweight. The influence of these and other possible risk factors can only be determined by comparing two equal-sized groups made up of randomly assigned participants.

That is why randomized controlled trials are usually the only suitable way to find out how effective a treatment is. Systematic reviews, which summarize multiple RCTs, are even better. In order to be good-quality, though, all studies and systematic reviews need to be designed properly and eliminate as many potential sources of error as possible.

• German Network for Evidence-based Medicine. Glossar: Qualitative Forschung.  Berlin: DNEbM; 2011. 
• Greenhalgh T. Einführung in die Evidence-based Medicine: kritische Beurteilung klinischer Studien als Basis einer rationalen Medizin. Bern: Huber; 2003. 
• Institute for Quality and Efficiency in Health Care (IQWiG, Germany). General methods . Version 5.0. Cologne: IQWiG; 2017.
• Klug SJ, Bender R, Blettner M, Lange S. Wichtige epidemiologische Studientypen. Dtsch Med Wochenschr 2007; 132:e45-e47. [ PubMed : 17530597 ]
• Schäfer T. Kritische Bewertung von Studien zur Ätiologie. In: Kunz R, Ollenschläger G, Raspe H, Jonitz G, Donner-Banzhoff N (eds.). Lehrbuch evidenzbasierte Medizin in Klinik und Praxis. Cologne: Deutscher Ärzte-Verlag; 2007.

IQWiG health information is written with the aim of helping people understand the advantages and disadvantages of the main treatment options and health care services.

Because IQWiG is a German institute, some of the information provided here is specific to the German health care system. The suitability of any of the described options in an individual case can be determined by talking to a doctor. We do not offer individual consultations.

Our information is based on the results of good-quality studies. It is written by a team of health care professionals, scientists and editors, and reviewed by external experts. You can find a detailed description of how our health information is produced and updated in our methods.

• Cite this Page InformedHealth.org [Internet]. Cologne, Germany: Institute for Quality and Efficiency in Health Care (IQWiG); 2006-. What types of studies are there? 2016 Jun 15 [Updated 2016 Sep 8].

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• Clinical Trials

About Clinical Studies

Research: it's all about patients.

Mayo's mission is about the patient, the patient comes first. So the mission and research here, is to advance how we can best help the patient, how to make sure the patient comes first in care. So in many ways, it's a cycle. It can start with as simple as an idea, worked on in a laboratory, brought to the patient bedside, and if everything goes right, and let's say it's helpful or beneficial, then brought on as a standard approach. And I think that is one of the unique characteristics of Mayo's approach to research, that patient-centeredness. That really helps to put it in its own spotlight.

At Mayo Clinic, the needs of the patient come first. Part of this commitment involves conducting medical research with the goal of helping patients live longer, healthier lives.

Through clinical studies, which involve people who volunteer to participate in them, researchers can better understand how to diagnose, treat and prevent diseases or conditions.

Types of clinical studies

• Observational study. A type of study in which people are observed or certain outcomes are measured. No attempt is made by the researcher to affect the outcome — for example, no treatment is given by the researcher.
• Clinical trial (interventional study). During clinical trials, researchers learn if a new test or treatment works and is safe. Treatments studied in clinical trials might be new drugs or new combinations of drugs, new surgical procedures or devices, or new ways to use existing treatments. Find out more about the five phases of non-cancer clinical trials on ClinicalTrials.gov or the National Cancer Institute phases of cancer trials .
• Medical records research. Medical records research involves the use of information collected from medical records. By studying the medical records of large groups of people over long periods of time, researchers can see how diseases progress and which treatments and surgeries work best. Find out more about Minnesota research authorization .

Clinical studies may differ from standard medical care

A health care provider diagnoses and treats existing illnesses or conditions based on current clinical practice guidelines and available, approved treatments.

But researchers are constantly looking for new and better ways to prevent and treat disease. In their laboratories, they explore ideas and test hypotheses through discovery science. Some of these ideas move into formal clinical trials.

During clinical studies, researchers formally and scientifically gather new knowledge and possibly translate these findings into improved patient care.

Before clinical trials begin

This video demonstrates how discovery science works, what happens in the research lab before clinical studies begin, and how a discovery is transformed into a potential therapy ready to be tested in trials with human participants:

How clinical trials work

Trace the clinical trial journey from a discovery research idea to a viable translatable treatment for patients:

See a glossary of terms related to clinical studies, clinical trials and medical research on ClinicalTrials.gov.

Watch a video about clinical studies to help you prepare to participate.

Let's Talk About Clinical Research

Narrator: This presentation is a brief introduction to the terms, purposes, benefits and risks of clinical research.

If you have questions about the content of this program, talk with your health care provider.

What is clinical research?

Clinical research is a process to find new and better ways to understand, detect, control and treat health conditions. The scientific method is used to find answers to difficult health-related questions.

Ways to participate

There are many ways to participate in clinical research at Mayo Clinic. Three common ways are by volunteering to be in a study, by giving permission to have your medical record reviewed for research purposes, and by allowing your blood or tissue samples to be studied.

Types of clinical research

There are many types of clinical research:

• Prevention studies look at ways to stop diseases from occurring or from recurring after successful treatment.
• Screening studies compare detection methods for common conditions.
• Diagnostic studies test methods for early identification of disease in those with symptoms.
• Treatment studies test new combinations of drugs and new approaches to surgery, radiation therapy and complementary medicine.
• The role of inheritance or genetic studies may be independent or part of other research.
• Quality of life studies explore ways to manage symptoms of chronic illness or side effects of treatment.
• Medical records studies review information from large groups of people.

Clinical research volunteers

Participants in clinical research volunteer to take part. Participants may be healthy, at high risk for developing a disease, or already diagnosed with a disease or illness. When a study is offered, individuals may choose whether or not to participate. If they choose to participate, they may leave the study at any time.

Research terms

You will hear many terms describing clinical research. These include research study, experiment, medical research and clinical trial.

Clinical trial

A clinical trial is research to answer specific questions about new therapies or new ways of using known treatments. Clinical trials take place in phases. For a treatment to become standard, it usually goes through two or three clinical trial phases. The early phases look at treatment safety. Later phases continue to look at safety and also determine the effectiveness of the treatment.

Phase I clinical trial

A small number of people participate in a phase I clinical trial. The goals are to determine safe dosages and methods of treatment delivery. This may be the first time the drug or intervention is used with people.

Phase II clinical trial

Phase II clinical trials have more participants. The goals are to evaluate the effectiveness of the treatment and to monitor side effects. Side effects are monitored in all the phases, but this is a special focus of phase II.

Phase III clinical trial

Phase III clinical trials have the largest number of participants and may take place in multiple health care centers. The goal of a phase III clinical trial is to compare the new treatment to the standard treatment. Sometimes the standard treatment is no treatment.

Phase IV clinical trial

A phase IV clinical trial may be conducted after U.S. Food and Drug Administration approval. The goal is to further assess the long-term safety and effectiveness of a therapy. Smaller numbers of participants may be enrolled if the disease is rare. Larger numbers will be enrolled for common diseases, such as diabetes or heart disease.

Clinical research sponsors

Mayo Clinic funds clinical research at facilities in Rochester, Minnesota; Jacksonville, Florida; and Arizona, and in the Mayo Clinic Health System. Clinical research is conducted in partnership with other medical centers throughout the world. Other sponsors of research at Mayo Clinic include the National Institutes of Health, device or pharmaceutical companies, foundations and organizations.

Clinical research at Mayo Clinic

Dr. Hugh Smith, former chair of Mayo Clinic Board of Governors, stated, "Our commitment to research is based on our knowledge that medicine must be constantly moving forward, that we need to continue our efforts to better understand disease and bring the latest medical knowledge to our practice and to our patients."

This fits with the term "translational research," meaning what is learned in the laboratory goes quickly to the patient's bedside and what is learned at the bedside is taken back to the laboratory.

Ethics and safety of clinical research

All clinical research conducted at Mayo Clinic is reviewed and approved by Mayo's Institutional Review Board. Multiple specialized committees and colleagues may also provide review of the research. Federal rules help ensure that clinical research is conducted in a safe and ethical manner.

Institutional review board

An institutional review board (IRB) reviews all clinical research proposals. The goal is to protect the welfare and safety of human subjects. The IRB continues its review as research is conducted.

Consent process

Participants sign a consent form to ensure that they understand key facts about a study. Such facts include that participation is voluntary and they may withdraw at any time. The consent form is an informational document, not a contract.

Study activities

Staff from the study team describe the research activities during the consent process. The research may include X-rays, blood tests, counseling or medications.

Study design

During the consent process, you may hear different phrases related to study design. Randomized means you will be assigned to a group by chance, much like a flip of a coin. In a single-blinded study, participants do not know which treatment they are receiving. In a double-blinded study, neither the participant nor the research team knows which treatment is being administered.

Some studies use an inactive substance called a placebo.

Multisite studies allow individuals from many different locations or health care centers to participate.

Remuneration

If the consent form states remuneration is provided, you will be paid for your time and participation in the study.

Some studies may involve additional cost. To address costs in a study, carefully review the consent form and discuss questions with the research team and your insurance company. Medicare may cover routine care costs that are part of clinical trials. Medicaid programs in some states may also provide routine care cost coverage, as well.

When considering participation in a research study, carefully look at the benefits and risks. Benefits may include earlier access to new clinical approaches and regular attention from a research team. Research participation often helps others in the future.

Risks/inconveniences

Risks may include side effects. The research treatment may be no better than the standard treatment. More visits, if required in the study, may be inconvenient.

Weigh your risks and benefits

Consider your situation as you weigh the risks and benefits of participation prior to enrolling and during the study. You may stop participation in the study at any time.

Ask questions

Stay informed while participating in research:

• Write down questions you want answered.
• If you do not understand, say so.
• If you have concerns, speak up.

Website resources are available. The first website lists clinical research at Mayo Clinic. The second website, provided by the National Institutes of Health, lists studies occurring in the United States and throughout the world.

Additional information about clinical research may be found at the Mayo Clinic Barbara Woodward Lips Patient Education Center and the Stephen and Barbara Slaggie Family Cancer Education Center.

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Clinical Studies in Depth

Learning all you can about clinical studies helps you prepare to participate.

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Types of Clinical Trials – [A Comprehensive Guide]

It is clinical trials that advance healthcare, serving as pivotal gateways to innovation. In addition to driving innovation, these meticulously designed studies also offer the chance to improve treatment outcomes. We’ll dive deep in the diverse landscape of clinical trials as we set out on this journey, shedding light on different types of clinical trials and therapeutic areas.

What are Clinical Trials?

Clinical trials are research studies conducted to evaluate the safety and efficacy of medical interventions, including treatments, drugs, devices, and therapeutic strategies, in humans. These trials are essential for determining whether a new intervention is safe, effective, and suitable for widespread use in patient populations.

5 Types of Clinical Trials:

Let’s unveil 5 most common types of clinical trials below:

Treatment Trials:

• Focus on testing new treatments, therapies, or interventions.
• Investigate the efficacy and safety of novel drugs, procedures, or combinations of treatments.
• Aim to identify more effective approaches for managing diseases or conditions.

Prevention Trials:

• Aim to prevent the onset or recurrence of diseases or health conditions.
• Evaluate interventions such as vaccines, medications, lifestyle modifications, or behavioral interventions.

Diagnostic Trials:

• Assess new diagnostic tools, tests, or procedures for identifying diseases or health conditions.
• Aim to improve early detection, accuracy, and efficiency in diagnosing medical conditions.

Screening Trials:

• Evaluate the effectiveness of screening methods for detecting diseases or health conditions in populations.
• Focus on early detection and intervention to improve treatment outcomes and prognosis.

Observational Trials:

• Observe and analyze participants over time to gather data on health outcomes, risk factors, or disease progression.
• Do not involve intervention or manipulation of variables, but rather focus on documenting natural history and patterns of diseases.

Different Types of Clinical Trial Studies:

Below-mentioned are the different types of Clinical trial studies that give you a clear picture of what comes under the roof of Clinical Trial Studies.

Randomized Controlled Trials (RCTs):

• Gold standard in clinical research .
• Participants are randomly assigned to different intervention groups to minimize bias and confounding factors.
• Rigorous design ensures reliable and robust evidence for assessing treatment effects.

Non-Randomized Trials:

• Participants are not randomly assigned to intervention groups.
• May lack the same level of control as RCTs but can provide valuable insights, particularly in real-world settings.

Cross-Over Trials:

• Participants receive different interventions sequentially, with a washout period in between.
• Designed to compare the effects of multiple treatments within the same group of participants.

Therapeutic Areas in Clinical Trials

Clinical trials span a wide range of therapeutic areas, reflecting the diverse landscape of medical research and healthcare needs. Some common therapeutic areas include:

• Cardiovascular diseases
• Infectious diseases
• Endocrinology
• Rare diseases

Ending Line:

Now that you know that the world of clinical trials is complex and dynamic, encompassing various types of studies and therapeutic areas. Each trial plays a crucial role in advancing medical knowledge, improving patient care, and shaping the future of healthcare.

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• Clinical Trials: What Patients Need to Know

What Are the Different Types of Clinical Research?

Different types of clinical research are used depending on what the researchers are studying. Below are descriptions of some different kinds of clinical research.

Treatment Research generally involves an intervention such as medication, psychotherapy, new devices, or new approaches to surgery or radiation therapy. 

Prevention Research looks for better ways to prevent disorders from developing or returning. Different kinds of prevention research may study medicines, vitamins, vaccines, minerals, or lifestyle changes. 

Diagnostic Research refers to the practice of looking for better ways to identify a particular disorder or condition. 

Screening Research aims to find the best ways to detect certain disorders or health conditions. 

Quality of Life Research explores ways to improve comfort and the quality of life for individuals with a chronic illness. 

Genetic studies aim to improve the prediction of disorders by identifying and understanding how genes and illnesses may be related. Research in this area may explore ways in which a person’s genes make him or her more or less likely to develop a disorder. This may lead to development of tailor-made treatments based on a patient’s genetic make-up. 

Epidemiological studies seek to identify the patterns, causes, and control of disorders in groups of people. 

An important note: some clinical research is “outpatient,” meaning that participants do not stay overnight at the hospital. Some is “inpatient,” meaning that participants will need to stay for at least one night in the hospital or research center. Be sure to ask the researchers what their study requires. 

Phases of clinical trials: when clinical research is used to evaluate medications and devices Clinical trials are a kind of clinical research designed to evaluate and test new interventions such as psychotherapy or medications. Clinical trials are often conducted in four phases. The trials at each phase have a different purpose and help scientists answer different questions. 

Phase I trials Researchers test an experimental drug or treatment in a small group of people for the first time. The researchers evaluate the treatment’s safety, determine a safe dosage range, and identify side effects. 

Phase II trials The experimental drug or treatment is given to a larger group of people to see if it is effective and to further evaluate its safety.

Phase III trials The experimental study drug or treatment is given to large groups of people. Researchers confirm its effectiveness, monitor side effects, compare it to commonly used treatments, and collect information that will allow the experimental drug or treatment to be used safely. 

Phase IV trials Post-marketing studies, which are conducted after a treatment is approved for use by the FDA, provide additional information including the treatment or drug’s risks, benefits, and best use.

Examples of other kinds of clinical research Many people believe that all clinical research involves testing of new medications or devices. This is not true, however. Some studies do not involve testing medications and a person’s regular medications may not need to be changed. Healthy volunteers are also needed so that researchers can compare their results to results of people with the illness being studied. Some examples of other kinds of research include the following: 

A long-term study that involves psychological tests or brain scans

A genetic study that involves blood tests but no changes in medication

A study of family history that involves talking to family members to learn about people’s medical needs and history.

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Clinical Trials

Clinical trials are research studies that involve people and test new ways to prevent, detect, diagnose, or treat diseases. Many medical procedures and treatments used today are the result of past clinical trials.

Taking part in a clinical trial has potential benefits and risks. The potential benefits of participating in a trial include the following:

• Trial participants have access to promising new procedures or treatments that are generally not available outside of a clinical trial.
• The new procedure or treatment being studied may be more effective than the current usual approach. If it is more effective, trial participants may be the first to benefit from it.
• Trial participants receive high-quality medical care from a research team that includes doctors, nurses, and other health professionals.
• The results of the trial may help other people who need medical care in the future.
• Trial participants are helping scientists learn more about cancer and other medical conditions, which will lead to more advances.

Some of the potential risks of taking part in a clinical trial are:

• The new procedure or drug may not be better than what is currently available, or it may have side effects that doctors do not expect or that are worse than the side effects of the current usual approach.
• Trial participants may be required to make more visits to the doctor than they would if they were not in a clinical trial and/or need to travel farther for those visits.
• Some of the costs of participating in a trial may not be covered by health insurance.

The decision to take part in a clinical trial is a personal one. Your health care team and your loved ones, if you wish, can assist you in deciding whether or not a clinical trial is right for you. The final decision, however, is yours alone to make.

Visit ClinicalTrials.gov to search for NIH-sponsored colorectal cancer clinical trials that are currently accepting patients.

This page last reviewed on August 20, 2015

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• Types of clinical trials

Medical research studies involving people are called clinical trials.

There are two main types of trials or studies - interventional and observational. 

Interventional trials aim to find out more about a particular intervention, or treatment. A computer puts people taking part into different treatment groups. This is so that the research team can compare the results.

Observational studies aim to find out what happens to people in different situations. The research team observe the people taking part, but they don’t influence what treatments people have. The people taking part aren’t put into treatment groups.

There are different types of trials within these two groups. This page has information about

Pilot studies and feasibility studies

Prevention trials, screening trials, treatment trials, multi-arm multi-stage (mams) trials, cohort studies.

Case control studies  

Cross sectional studies  

Pilot studies and feasibility studies are small versions of studies which are sometimes done before a large trial takes place.

Feasibility studies are designed to see if it is possible to do the main study.  They aim to find out things such as whether patients and doctors are happy to take part, and how long it might take to collect and analyse the information. They don’t answer the main research question about how well a treatment works. 

Pilot studies are small versions of the main study. Pilot studies help to test that all the main parts of the study work together. They may also help answer the research question. Sometimes the research team include the information collected during the pilot study in the results of the main study. 

Prevention trials look at whether a particular treatment can help prevent cancer. The people taking part don't have cancer. 

These trials can be for the general population or for people who have a higher than normal risk of developing a certain cancer. For example, this could include people with a strong family history of cancer. 

Screening tests people for the early signs of cancer before they have any symptoms. As with prevention trials, screening trials can be for the general population. Or they can be for a group of people who have a higher than normal risk of developing a certain cancer.

Researchers may plan screening trials to see if new tests are reliable enough to detect particular types of cancer. Or they may try to find out if there is an overall benefit in picking up the cancer early.

For trials that compare two or more treatments, you are put into a treatment group at random. This is a randomised trial. They are the best way to get reliable information about how well a new treatment works. We have more information about randomisation .

A multi arm trial is a trial that has:

• several treatment groups as well as

Multi-arm multi-stage (MAMS) trials have the same control group all the way through. The other treatment groups can change as the trial goes on. As these trials are more complex there are a number of treatments that people might have. 

The research team may decide to stop recruiting people to a particular group. This could be because they have enough people to start looking at the results. Or because early results show the treatment isn’t working as well as they’d hoped.

The researchers may add new treatment groups as new drugs become available to look at. This means they don’t have to design and launch a brand new trial each time they want to research a new treatment. So it helps get results quicker.

The Stampede trial for prostate cancer is an example of a MAMS trial.

Observational studies Cohort studies, case control studies and cross sectional studies are all types of observational studies.

A cohort is a group of people, so cohort studies look at groups of people. A cohort study follows the group over a period of time. 

A research team may recruit people who do not have cancer and collect information about them for a number of years. The researchers see who in the group develops cancer and who doesn’t. They then look to see whether the people who developed cancer had anything in common.

Cohort studies are very useful ways of finding out more about risk factors. But they are expensive and time consuming. They can be used when it wouldn’t be possible to test a theory any other way. 

Case control studies

Case control studies work the opposite way to cohort studies. The research team recruits a group of people who have a disease (cases) and a group of people who don't (controls). They then look back to see how many people in each group were exposed to a certain risk factor. 

Researchers want to make the results as reliable as possible. So they try to make sure the people in each group have the same general factors such as age or gender.

Case control studies are useful and they are quicker and cheaper than cohort studies. But the results may be less reliable. The research team often rely on people thinking back and remembering whether they were exposed to a certain risk factor or not. But people may not remember accurately, and this can affect the results.

Another issue is the difference between association and cause. Just because there is an association between a factor and a disease, it doesn’t mean that the factor causes the disease.

For example, a case control study may show that people with a lower income are more likely to develop cancer. But it doesn’t mean that the level of income itself causes cancer. It may mean that they have a poor diet or are more likely to smoke.

Cross sectional studies

Cross sectional studies are carried out at one point in time, or over a short period of time. They find out who has been exposed to a risk factor and who has developed cancer, and see if there is a link. 

Cross sectional studies are quicker and cheaper to do. But the results can be less useful. Sometimes researchers do a cross sectional study first to find a possible link. Then they go on to do a case control or cohort study to look at the issue in more detail.

Oxford Handbook of Clinical and Healthcare Research (1st edition) R Sumantra, S Fitzpatrick, R Golubic and others Oxford University Press, 2016

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Clinical Trials Information for Patients and Caregivers

Explore the basics of clinical trials, including what they are, how they work, and what to expect. Learning all you can about clinical trials can help you talk with your doctor and decide what is right for you.

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Find an NCI-supported clinical trial—and learn how to locate other research studies—that may be right for you or a loved one.

What Are Clinical Trials?

Learn about the purpose and importance of clinical trials, including the different types of clinical trials used in cancer research.

How Do Clinical Trials Work?

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Who Pays for Clinical Trials?

Learn about the types of costs related to participating in a clinical trial, who is expected to pay for which costs, and tips for working with insurance companies.

Are Clinical Trials Safe?

Learn about the federal rules to help protect the rights and safety of people who take part in clinical trials. Learn about informed consent, institutional review boards, and how trials are closely monitored.

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Types of Research Studies

Epidemiology studies.

Epidemiology is the study of the patterns and causes of disease in people.

The goal of epidemiology studies is to give information that helps support or disprove an idea about a possible link between an exposure (such as alcohol use) and an outcome (such as breast cancer) in people.

The 2 main types of epidemiology studies are:

• Observational studies ( prospective cohort or case-control )

Randomized controlled trials

Though they have the same goal, observational studies and randomized controlled trials differ in:

• The way they are conducted
• The strengths of the conclusions they reach

Observational studies

In observational studies, the people in the study live their daily lives as they choose. They exercise when they want, eat what they like and take the medicines their doctors prescribe. They report these activities to researchers.

There are 2 types of observational studies:

Prospective cohort studies

Case-control studies.

A prospective cohort study follows a large group of people forward in time.

Some people will have a certain exposure (such as alcohol use) and others will not.

Researchers compare the different groups (for example, they might compare heavy drinkers, moderate drinkers, light drinkers and non-drinkers) to see which group is more likely to develop an outcome (such as breast cancer).

In a case-control study, researchers identify 2 groups: cases and controls.

• Cases are people who already have an outcome (such as breast cancer).
• Controls are people who do not have the outcome.

The researchers compare the 2 groups to see if any exposure (such as alcohol use) was more common in the history of one group compared to the other.

In randomized controlled trials (randomized clinical trials), researchers divide people into groups to compare different treatments or other interventions.

These studies are called randomized controlled trials because people are randomly assigned (as if by coin toss) to a certain treatment or behavior.

For example, in a randomized trial of a new drug therapy, half the people might be randomly assigned to a new drug and the other half to the standard treatment.

In a randomized controlled trial on exercise and breast cancer risk, half the participants might be randomly assigned to walk 10 minutes a day and the other half to walk 2 hours a day. The researchers would then see which group was more likely to develop breast cancer, those who walked 10 minutes a day or those who walked 2 hours a day.

Many behaviors, such as smoking or heavy alcohol drinking, can’t be tested in this way because it isn’t ethical to assign people to a behavior known to be harmful. In these cases, researchers must use observational studies.

Patient series

A patient series is a doctor’s observations of a group of patients who are given a certain treatment.

There is no comparison group in a patient series. All the patients are given a certain treatment and the outcomes of these patients are studied.

With no comparison group, it’s hard to draw firm conclusions about the effectiveness of a treatment.

For example, if 10 women with breast cancer are given a new treatment, and 2 of them respond, how do we know if the new treatment is better than standard treatment?

If we had a comparison group of 10 women with breast cancer who got standard treatment, we could compare their outcomes to those of the 10 women on the new treatment. If no women in the comparison group responded to standard treatment, then the 2 women who responded to the new treatment would represent a success of the new treatment. If, however, 2 of the 10 women in the standard treatment group also responded, then the new treatment is no better than the standard.

The lack of a comparison group makes it hard to draw conclusions from a patient series. However, data from a patient series can help form hypotheses that can be tested in other types of studies.

Strengths and weaknesses of different types of research studies

When reviewing scientific evidence, it’s helpful to understand the strengths and weaknesses of different types of research studies.

Case-control studies have some strengths:

• They are easy and fairly inexpensive to conduct.
• They are a good way for researchers to study rare diseases. If a disease is rare, you would need to follow a very large group of people forward in time to have many cases of the disease develop.
• They are a good way for researchers to study diseases that take a long time to develop. If a disease takes a long time to develop, you would have to follow a group of people for many years for cases of the disease to develop.

Case-control studies look at past exposures of people who already have a disease. This causes some concerns:

• It can be hard for people to remember details about the past, especially when it comes to things like diet.
• Memories can be biased (or influenced) because the information is gathered after an event, such as the diagnosis of breast cancer.
• When it comes to sensitive topics (such as abortion), the cases (the people with the disease) may be much more likely to give complete information about their history than the controls (the people without the disease). Such differences in reporting bias study results.

For these reasons, the accuracy of the results of case-control studies can be questionable.

Cohort studies

Prospective cohort studies avoid many of the problems of case-control studies because they gather information from people over time and before the events being studied happen.

However, compared to case-control studies, they are expensive to conduct.

Nested case-control studies

A nested case-control study is a case-control study within a prospective cohort study.

Nested case-control studies use the design of a case-control study. However, they use data gathered as part of a cohort study, so they are less prone to bias than standard case-control studies.

All things being equal, the strength of nested case-control data falls somewhere between that of standard case-control studies and cohort studies.

Randomized controlled trials are considered the gold standard for studying certain exposures, such as breast cancer treatment. Similar to cohort studies, they follow people over time and are expensive to do.

Because people in a randomized trial are randomly assigned to an intervention (such as a new chemotherapy drug) or standard treatment, these studies are more likely to show the true link between an intervention and a health outcome (such as survival).

Learn more about randomized clinical trials , including the types of clinical trials, benefits, and possible drawbacks.

Overall study quality

The overall quality of a study is important. For example, the results from a well-designed case-control study can be more reliable than those from a poorly-designed randomized trial.

Finding more information on research study design

If you’re interested in learning more about research study design, a basic epidemiology textbook from your local library may be a good place to start. The National Cancer Institute also has information on epidemiology studies and randomized controlled trials.

Animal studies

Animal studies add to our understanding of how and why some factors cause cancer in people.

However, there are many differences between animals and people, so it makes it hard to translate findings directly from one to the other.

Animal studies are also designed differently. They often look at exposures in larger doses and for shorter periods of time than are suitable for people.

While animal studies can lay the groundwork for research in people, we need human studies to draw conclusions for people.

All data presented within this section of the website come from studies done with people.

Joining a research study

Research is ongoing to improve all areas of breast cancer, from prevention to treatment.

Whether you’re newly diagnosed, finished breast cancer treatment many years ago, or even if you’ve never had breast cancer, there may be breast cancer research studies you can join.

If you have breast cancer, BreastCancerTrials.org in collaboration with Susan G. Komen® offers a custom matching service that can help find a studies that fit your needs. You can also visit the National Institutes of Health’s website to find a breast cancer treatment study.

If you’re interested in being part of other studies, talk with your health care provider. Your provider may know of studies in your area looking for volunteers.

Learn more about joining a research study .

Learn more about clinical trials .

Learn what Komen is doing to help people find and participate in clinical trials .

Updated 12/16/20

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What are some different types of research studies?

There are many different types of research studies. Generally, there are two major types of studies available on Research for Me @UNC: research studies and clinical trials . When a research study is about disease or human health, it is called a clinical research study. When a research study involves drugs or other therapies that aim to slow or stop a disease, then it is called a clinical trial. Volunteers are an important part of all of these research studies! Explore other types of research studies below. 

Survey - Survey studies ask people questions about their knowledge, attitudes, and feelings about a wide range of topics. You can complete these surveys online, over the phone, or by mail. Sometimes, these studies might also be in-person interviews or group discussions.

Lifestyle - Lifestyle studies look at what happens when people participate in different types of activities over a set period of time. You may attend activity sessions in a center or clinic or be asked to change the way that you do something in your daily activities. Often, these studies are interested in how changes in behavior can affect our health or other parts of our lives.

Drug - Drug studies are heavily regulated by the US Government. Studies often involve medications that are not currently available to the general public. They are called “investigational” drugs and have not yet been approved by the FDA (US Food and Drug Administration) for your normal health care provider to prescribe. Other drug studies may involve comparisons between two or more FDA-approved medications.

Device - Device studies are done to learn if a new medical device helps relieve a certain medical condition. Devices you may be familiar with are pacemakers, diabetes testing meters, and hearing aids. These studies usually involve devices that are not currently available to the general public and have not been approved for use by the FDA. Sometimes, they may be studying an FDA-approved device, but for use in treating a new condition. 

Procedure - Procedure studies learn about the safety and effectiveness of certain medical procedures. Sometimes they compare a new medical procedure to one already in use. Procedures might include things like imaging (x-rays), stitches, blood tests, and surgeries.

Medical Outcomes - Outcomes research studies the end results (outcomes) of the structure and processes of the health care system on the health and well-being of patients and populations. These studies look at clinical practices to see if there are better ways for doctors to help patients manage their medical care. Outcomes research often considers patients’ experiences, preferences, and values – all of which may affect whether or not a medical treatment is best for them. 

Community-based - Community-based research is done through a true partnership of community leaders and organizations with a UNC researcher or research team. The ideas are driven by community members and the research incorporates voices of all involved.  These studies aim to understand problems impacting communities and contribute to solutions through policy or social change. 

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Types of Clinical Trials

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What Are the Different Types of Clinical Research?

What are the different types of clinical studies.

Clinical research is medical research that is done in human participants after initial (“preclinical”) research has been conducted in a lab setting. 1, 2 All research that involves people is called clinical research. 3 The purpose of clinical research is to advance medicine.

There are many different types of clinical research, each aimed at advancing medicine in a different way.

Clinical research can be divided into different categories based on the questions that the researchers are trying to answer. 3

Epidemiology

Epidemiology involves studying patterns, causes, and effects of both health and disease among different groups in a population. 3, 4 This allows researchers to better understand diseases.

Behavioral research involves studying human behavior and how it is associated with both health and disease. 3

Health Services

Health services research involves studying the use and costs of healthcare services and the health outcomes of people that use them. 3

Prevention research involves the study of ways to prevent a disorder from developing or returning. 3,4 Interventions can include medicines, medical devices, vaccines, vitamins or minerals, and behavioral changes.

Screening research involves the study of ways to identify disorders early on, sometimes even before there are any symptoms. 2, 3, 4, 5

Diagnostic research involves the study of tests or procedures for diagnosing a disorder or condition. 3, 4, 5

Treatment research involves the study of ways to treat a particular disorder. 4, 5 Interventions can include medicines, medical devices, procedures, and behavioral changes. 3, 4, 5 While some treatments may be new, existing treatments may also be used in new ways. Examples of new ways to use existing treatments include combining them with other medicines or using them to treat a different disorder. 3

Quality of Life

Quality of life research, sometimes called supportive care research, involves the study of ways to increase comfort and improve quality of life for people living with long-lasting conditions or illnesses. 2, 3, 5 Sometimes this research involves managing the symptoms of a condition or the side effects that can occur from treating the condition. 6

Clinical trials and observational studies are the two main types of clinical studies. 5

Clinical Trials

Clinical trials are also sometimes called interventional studies. 5 Clinical trials help researchers like scientists and doctors test new ways to prevent, screen, diagnose, treat, or manage a medical condition. 3. 4. 6 Clinical trials are carefully planned by the researchers. 7 Participants in clinical trials are separated into groups and assigned a specific treatment. 3, 5 Examples of treatments are medicines, vaccines, medical devices, procedures, or behavioral changes. 3, 4, 5 Researchers then use different tests to measure how the treatment affects the participants’ health. 5, 7

Clinical trials are often divided into a sequence of four phases. 4, 7 Each phase of a trial has a different purpose. The Food and Drug Administration, or FDA, approves advancement to each phase of a clinical trial. 5 Clinical trials vary in size and can include people of all ages who are healthy or affected by the condition being studied. 4, 7 Well-designed clinical trials are often considered the best way to find out whether a treatment works. 3

Clinical Trial Example One clinical trial aimed to find out if a specific relaxation technique could help reduce anxiety and pain in children having minor surgery. 7 The children were separated into two groups: one group used the relaxation technique and the other group received the usual care. The researchers used the data they collected to find out if the relaxation technique reduced anxiety before surgery and pain after surgery any better than the usual care did.

Observational Studies

Some research questions might require a different type of clinical study. 3, 8   Observational studies are another type of clinical study that can be used to answer research questions. 5, 9 Observational studies help researchers understand a situation by observing people in normal settings. 2, 3 Observational studies are different from clinical trials in that participants in observational studies are not assigned to a specific treatment. 3, 5, 9 Instead, participants may receive a treatment as part of their usual medical care or be exposed to something in their everyday lives. 5, 9 Researchers collect data and identify associations between factors by comparing the changes that they observe over time. 2, 3

Researchers may use observational studies instead of randomized controlled clinical trials for different reasons. One prominent reason is that certain interventions may be unethical to test. 9 In addition, observational studies are generally much less expensive to run and may be more appropriate for determining effects over a long range of time. 10

Observational studies can have a variety of designs. 3, 9 Some common types of observational studies include cohort, case-control, and cross-sectional studies.

Cohort Study

In a cohort study, a large group of people is observed over time. 3, 9 Some people might naturally develop a disease or condition during this time period. This type of study can help find links between risk factors and disorders.

Cohort Study Example The Nurses’ Health Study was started in 1976 and is a classic example of a cohort study. 11 This study has examined the risk factors for major chronic diseases in more than 280,000 women.

Case-Control Study

Case-control studies consist of people who have a condition or disease and people who don’t. 3, 9 This type of study can help find links between disorders and past exposures to risk factors.

Case-Control Study Example One case-control study evaluated previous use of a pain reliever medicine in individuals who had kidney disease and people who did not have kidney disease. 12 The researchers used the data they collected to find out if a pain reliever medicine might be associated with an increased risk of kidney disease.

Cross-Sectional Study

A cross-sectional study is a snapshot of a population at a single moment in time. 3, 9 This study design is used to determine the prevalence of a condition or disease, risk factors, and exposures.

Cross-Sectional Study Example A cross-sectional study has evaluated the association between banana consumption and depressive symptoms in Chinese adults. 13

Researchers can use the results of observational studies to gather ideas about the research questions that can be answered in clinical trials. 2, 3

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1.3: Types of Research Studies and How To Interpret Them

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• Alice Callahan, Heather Leonard, & Tamberly Powell
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The field of nutrition is dynamic, and our understanding and practices are always evolving. Nutrition scientists are continuously conducting new research and publishing their findings in peer-reviewed journals. This adds to scientific knowledge, but it’s also of great interest to the public, so nutrition research often shows up in the news and other media sources. You might be interested in nutrition research to inform your own eating habits, or if you work in a health profession, so that you can give evidence-based advice to others. Making sense of science requires that you understand the types of research studies used and their limitations.

The Hierarchy of Nutrition Evidence

Researchers use many different types of study designs depending on the question they are trying to answer, as well as factors such as time, funding, and ethical considerations. The study design affects how we interpret the results and the strength of the evidence as it relates to real-life nutrition decisions. It can be helpful to think about the types of studies within a pyramid representing a hierarchy of evidence, where the studies at the bottom of the pyramid usually give us the weakest evidence with the least relevance to real-life nutrition decisions, and the studies at the top offer the strongest evidence, with the most relevance to real-life nutrition decisions .

Figure 2.1. Hierarchy of research design and levels of scientific evidence with the strongest studies at the top and the weakest at the bottom.

The pyramid also represents a few other general ideas. There tend to be more studies published using the methods at the bottom of the pyramid, because they require less time, money, and other resources. When researchers want to test a new hypothesis , they often start with the study designs at the bottom of the pyramid , such as in vitro, animal, or observational studies. Intervention studies are more expensive and resource-intensive, so there are fewer of these types of studies conducted. But they also give us higher quality evidence, so they’re an important next step if observational and non-human studies have shown promising results. Meta-analyses and systematic reviews combine the results of many studies already conducted, so they help researchers summarize scientific knowledge on a topic.

Non-Human Studies: In Vitro & Animal Studies

The simplest form of nutrition research is an in vitro study . In vitro means “within glass,” (although plastic is used more commonly today) and these experiments are conducted within flasks, dishes, plates, and test tubes. One common form of in vitro research is cell culture. This involves growing cells in flasks and dishes. In order for cells to grow, they need a nutrient source. For cell culture, the nutrient source is referred to as media. Media supplies nutrients to the cells in vitro similarly to how blood performs this function within the body. Most cells adhere to the bottom of the flask and are so small that a microscope is needed to see them. The cells are grown inside an incubator, which is a device that provides the optimal temperature, humidity, and carbon dioxide (CO2CO2) concentrations for cells and microorganisms. By imitating the body's temperature and CO2CO2 levels (37 degrees Celsius, 5% CO2CO2), the incubator allows cells to grow even though they are outside the body.

A limitation of in vitro research compared to in vivo research is that it typically does not take digestion or bioavailability into account. This means that the concentration used might not be physiologically possible (it might be much higher) and that digestion and metabolism of what is being provided to cells may not be taken into account. Cell-based in vitro research is not as complex of a biological system as animals or people that have tissues, organs, etc. working together as well.

Since these studies are performed on isolated cells or tissue samples, they are less expensive and time-intensive than animal or human studies. In vitro studies are vital for zooming in on biological mechanisms, to see how things work at the cellular or molecular level. However, these studies shouldn’t be used to draw conclusions about how things work in humans (or even animals), because we can’t assume that the results will apply to a whole, living organism.

Animal studies are one form of in vivo research, which translates to “within the living.” Rats and mice are the most common animals used in nutrition research. Animals are often used in research that would be unethical to conduct in humans. Another advantage of animal dietary studies is that researchers can control exactly what the animals eat. In human studies, researchers can tell subjects what to eat and even provide them with the food, but they may not stick to the planned diet. People are also not very good at estimating, recording, or reporting what they eat and in what quantities. In addition, animal studies typically do not cost as much as human studies.

There are some important limitations of animal research. First, an animal’s metabolism and physiology are different from humans. Plus, animal models of disease (cancer, cardiovascular disease, etc.), although similar, are different from human diseases. Animal research is considered preliminary, and while it can be very important to the process of building scientific understanding and informing the types of studies that should be conducted in humans, animal studies shouldn’t be considered relevant to real-life decisions about how people eat.

Observational Studies

Observational studies in human nutrition collect information on people’s dietary patterns or nutrient intake and look for associations with health outcomes. Observational studies do not give participants a treatment or intervention; instead, they look at what they’re already doing and see how it relates to their health. These types of study designs can only identify correlations (relationships) between nutrition and health; they can’t show that one factor causes another. (For that, we need intervention studies, which we’ll discuss in a moment.) Observational studies that describe factors correlated with human health are also called epidemiological studies . 1

Epidemiology is defined as the study of human populations. These studies often investigate the relationship between dietary consumption and disease development. There are three main types of epidemiological studies: cross-sectional, case-control, and prospective cohort studies.

One example of a nutrition hypothesis that has been investigated using observational studies is that eating a Mediterranean diet reduces the risk of developing cardiovascular disease. (A Mediterranean diet focuses on whole grains, fruits and vegetables, beans and other legumes, nuts, olive oil, herbs, and spices. It includes small amounts of animal protein (mostly fish), dairy, and red wine. 2 ) There are three main types of observational studies, all of which could be used to test hypotheses about the Mediterranean diet:

• Cohort studies follow a group of people (a cohort) over time, measuring factors such as diet and health outcomes. A cohort study of the Mediterranean diet would ask a group of people to describe their diet, and then researchers would track them over time to see if those eating a Mediterranean diet had a lower incidence of cardiovascular disease.
• Case-control studies compare a group of cases and controls, looking for differences between the two groups that might explain their different health outcomes. For example, researchers might compare a group of people with cardiovascular disease with a group of healthy controls to see whether there were more controls or cases that followed a Mediterranean diet.
• Cross-sectional studies collect information about a population of people at one point in time. For example, a cross-sectional study might compare the dietary patterns of people from different countries to see if diet correlates with the prevalence of cardiovascular disease in the different countries.

There are two types of cohort studies: retrospective and prospective. Retrospective studies look at what happened in the past, and they’re considered weaker because they rely on people’s memory of what they ate or how they felt in the past. Prospective cohort studies, which enroll a cohort and follow them into the future, are usually considered the strongest type of observational study design.

Most cohort studies are prospective. Initial information is collected (usually by food frequency questionnaires) on the intake of a cohort of people at baseline, or the beginning. This cohort is then followed over time (normally many years) to quantify health outcomes of the individual within it. Cohort studies are normally considered to be more robust than case-control studies, because these studies do not start with diseased people and normally do not require people to remember their dietary habits in the distant past or before they developed a disease. An example of a prospective cohort study would be if you filled out a questionnaire on your current dietary habits and are then followed into the future to see if you develop osteoporosis. As shown below, instead of separating based on disease versus disease-free, individuals are separated based on exposure. In this example, those who are exposed are more likely to be diseased than those who were not exposed.

Using trans-fat intake again as the exposure and cardiovascular disease as the disease, the figure would be expected to look like this:

There are several well-known examples of prospective cohort studies that have described important correlations between diet and disease:

• Framingham Heart Study : Beginning in 1948, this study has followed the residents of Framingham, Massachusetts to identify risk factors for heart disease.
• Health Professionals Follow-Up Study : This study started in 1986 and enrolled 51,529 male health professionals (dentists, pharmacists, optometrists, osteopathic physicians, podiatrists, and veterinarians), who complete diet questionnaires every 2 years.
• Nurses Health Studies : Beginning in 1976, these studies have enrolled three large cohorts of nurses with a total of 280,000 participants. Participants have completed detailed questionnaires about diet, other lifestyle factors (smoking and exercise, for example), and health outcomes.

Observational studies have the advantage of allowing researchers to study large groups of people in the real world, looking at the frequency and pattern of health outcomes and identifying factors that correlate with them. But even very large observational studies may not apply to the population as a whole. For example, the Health Professionals Follow-Up Study and the Nurses Health Studies include people with above-average knowledge of health. In many ways, this makes them ideal study subjects, because they may be more motivated to be part of the study and to fill out detailed questionnaires for years. However, the findings of these studies may not apply to people with less baseline knowledge of health.

We’ve already mentioned another important limitation of observational studies—that they can only determine correlation, not causation. A prospective cohort study that finds that people eating a Mediterranean diet have a lower incidence of heart disease can only show that the Mediterranean diet is correlated with lowered risk of heart disease. It can’t show that the Mediterranean diet directly prevents heart disease. Why? There are a huge number of factors that determine health outcomes such as heart disease, and other factors might explain a correlation found in an observational study. For example, people who eat a Mediterranean diet might also be the same kind of people who exercise more, sleep more, have a higher income (fish and nuts can be expensive!), or be less stressed. These are called confounding factors ; they’re factors that can affect the outcome in question (i.e., heart disease) and also vary with the factor being studied (i.e., Mediterranean diet).

Intervention Studies

Intervention studies , also sometimes called experimental studies or clinical trials, include some type of treatment or change imposed by the researcher. Examples of interventions in nutrition research include asking participants to change their diet, take a supplement, or change the time of day that they eat. Unlike observational studies, intervention studies can provide evidence of cause and effect , so they are higher in the hierarchy of evidence pyramid.

Randomization: The gold standard for intervention studies is the randomized controlled trial (RCT) . In an RCT, study subjects are recruited to participate in the study. They are then randomly assigned into one of at least two groups, one of which is a control group (this is what makes the study controlled ).

Randomization is the process of randomly assigning subjects to groups to decrease bias. Bias is a systematic error that may influence results. Bias can occur in assigning subjects to groups in a way that will influence the results. An example of bias in a study of an antidepressant drug is shown below. In this nonrandomized antidepressant drug example, researchers (who know what the subjects are receiving) put depressed subjects into the placebo group, while "less depressed" subjects are put into the antidepressant drug group. As a result, even if the drug isn't effective, the group assignment may make the drug appear effective, thus biasing the results as shown below.

This is a bit of an extreme example, but even if the researchers are trying to prevent bias, sometimes bias can still occur. However, if the subjects are randomized, the sick and the healthy people will ideally be equally distributed between the groups. Thus, the trial will be unbiased and a true test of whether or not the drug is effective.

Here is another example. In an RCT to study the effects of the Mediterranean diet on cardiovascular disease development, researchers might ask the control group to follow a low-fat diet (typically recommended for heart disease prevention) and the intervention group to eat a Mediterranean diet. The study would continue for a defined period of time (usually years to study an outcome like heart disease), at which point the researchers would analyze their data to see if more people in the control or Mediterranean diet had heart attacks or strokes. Because the treatment and control groups were randomly assigned, they should be alike in every other way except for diet, so differences in heart disease could be attributed to the diet. This eliminates the problem of confounding factors found in observational research, and it’s why RCTs can provide evidence of causation, not just correlation.

Imagine for a moment what would happen if the two groups weren’t randomly assigned. What if the researchers let study participants choose which diet they’d like to adopt for the study? They might, for whatever reason, end up with more overweight people who smoke and have high blood pressure in the low-fat diet group, and more people who exercised regularly and had already been eating lots of olive oil and nuts for years in the Mediterranean diet group. If they found that the Mediterranean diet group had fewer heart attacks by the end of the study, they would have no way of knowing if this was because of the diet or because of the underlying differences in the groups. In other words, without randomization, their results would be compromised by confounding factors, with many of the same limitations as observational studies.

Placebo: In an RCT of a supplement, the control group would receive a placebo—a “fake” treatment that contains no active ingredients, such as a sugar pill. The use of a placebo is necessary in medical research because of a phenomenon known as the placebo effect. The placebo effect results in a beneficial effect because of a subject’s belief in the treatment, even though there is no treatment actually being administered. An example would be an athlete who consumes a sports drink and runs the 100-meter dash in 11.00 seconds. The athlete then, under the exact same conditions, drinks what he is told is "Super Duper Sports Drink" and runs the 100-meter dash in 10.50 seconds. But what the athlete didn't know was that Super Duper Sports Drink was the Sports Drink + Food Coloring. There was nothing different between the drinks, but the athlete believed that the "Super Duper Sports Drink" was going to help him run faster, so he did. This improvement is due to the placebo effect.

Blinding is a technique to prevent bias in intervention studies. In a study without blinding, the subject and the researchers both know what treatment the subject is receiving. This can lead to bias if the subject or researcher has expectations about the treatment working, so these types of trials are used less frequently. It’s best if a study is double-blind , meaning that neither the researcher nor the subject knows what treatment the subject is receiving. It’s relatively simple to double-blind a study where subjects are receiving a placebo or treatment pill because they could be formulated to look and taste the same. In a single-blind study , either the researcher or the subject knows what treatment they’re receiving, but not both. Studies of diets—such as the Mediterranean diet example—often can’t be double-blinded because the study subjects know whether or not they’re eating a lot of olive oil and nuts. However, the researchers who are checking participants’ blood pressure or evaluating their medical records could be blinded to their treatment group, reducing the chance of bias.

Open-label study:

Single-blinded study:

Double-blinded study:

Like all studies, RCTs and other intervention studies do have some limitations. They can be difficult to carry on for long periods of time and require that participants remain compliant with the intervention. They’re also costly and often have smaller sample sizes. Furthermore, it is unethical to study certain interventions. (An example of an unethical intervention would be to advise one group of pregnant mothers to drink alcohol to determine its effects on pregnancy outcomes because we know that alcohol consumption during pregnancy damages the developing fetus.)

VIDEO: “ Not all scientific studies are created equal ” by David H. Schwartz, YouTube (April 28, 2014), 4:26.

Meta-Analyses and Systematic Reviews

At the top of the hierarchy of evidence pyramid are systematic reviews and meta-analyses . You can think of these as “studies of studies.” They attempt to combine all of the relevant studies that have been conducted on a research question and summarize their overall conclusions. Researchers conducting a systematic review formulate a research question and then systematically and independently identify, select, evaluate, and synthesize all high-quality evidence that relates to the research question. Since systematic reviews combine the results of many studies, they help researchers produce more reliable findings. A meta-analysis is a type of systematic review that goes one step further, combining the data from multiple studies and using statistics to summarize it, as if creating a mega-study from many smaller studies . 4

However, even systematic reviews and meta-analyses aren’t the final word on scientific questions. For one thing, they’re only as good as the studies that they include. The Cochrane Collaboration is an international consortium of researchers who conduct systematic reviews in order to inform evidence-based healthcare, including nutrition, and their reviews are among the most well-regarded and rigorous in science. For the most recent Cochrane review of the Mediterranean diet and cardiovascular disease, two authors independently reviewed studies published on this question. Based on their inclusion criteria, 30 RCTs with a total of 12,461 participants were included in the final analysis. However, after evaluating and combining the data, the authors concluded that “despite the large number of included trials, there is still uncertainty regarding the effects of a Mediterranean‐style diet on cardiovascular disease occurrence and risk factors in people both with and without cardiovascular disease already.” Part of the reason for this uncertainty is that different trials found different results, and the quality of the studies was low to moderate. Some had problems with their randomization procedures, for example, and others were judged to have unreliable data. That doesn’t make them useless, but it adds to the uncertainty about this question, and uncertainty pushes the field forward towards more and better studies. The Cochrane review authors noted that they found seven ongoing trials of the Mediterranean diet, so we can hope that they’ll add more clarity to this question in the future. 5

Science is an ongoing process. It’s often a slow process, and it contains a lot of uncertainty, but it’s our best method of building knowledge of how the world and human life works. Many different types of studies can contribute to scientific knowledge. None are perfect—all have limitations—and a single study is never the final word on a scientific question. Part of what advances science is that researchers are constantly checking each other’s work, asking how it can be improved and what new questions it raises.

Attributions:

• “Chapter 1: The Basics” from Lindshield, B. L. Kansas State University Human Nutrition (FNDH 400) Flexbook. goo.gl/vOAnR , CC BY-NC-SA 4.0
• “The Broad Role of Nutritional Science,” section 1.3 from the book An Introduction to Nutrition (v. 1.0), CC BY-NC-SA 3.0

References:

• 1 Thiese, M. S. (2014). Observational and interventional study design types; an overview. Biochemia Medica , 24 (2), 199–210. https://doi.org/10.11613/BM.2014.022
• 2 Harvard T.H. Chan School of Public Health. (2018, January 16). Diet Review: Mediterranean Diet . The Nutrition Source. https://www.hsph.harvard.edu/nutritionsource/healthy-weight/diet-reviews/mediterranean-diet/
• 3 Ross, R., Gray, C. M., & Gill, J. M. R. (2015). Effects of an Injected Placebo on Endurance Running Performance. Medicine and Science in Sports and Exercise , 47 (8), 1672–1681. https://doi.org/10.1249/MSS.0000000000000584
• 4 Hooper, A. (n.d.). LibGuides: Systematic Review Resources: Systematic Reviews vs Other Types of Reviews . Retrieved February 7, 2020, from //libguides.sph.uth.tmc.edu/c.php?g=543382&p=5370369
• 5 Rees, K., Takeda, A., Martin, N., Ellis, L., Wijesekara, D., Vepa, A., Das, A., Hartley, L., & Stranges, S. (2019). Mediterranean‐style diet for the primary and secondary prevention of cardiovascular disease. Cochrane Database of Systematic Reviews , 3 . doi.org/10.1002/14651858.CD009825.pub3
• 6Levin K. (2006) Study design III: Cross-sectional studies. Evidence - Based Dentistry 7(1): 24.
• Figure 2.3. The hierarchy of evidence by Alice Callahan, is licensed under CC BY 4.0
• Research lab photo by National Cancer Institute on Unsplas h ; mouse photo by vaun0815 on Unsplash
• Figure 2.4. “Placebo effect example” by Lindshield, B. L. Kansas State University Human Nutrition (FNDH 400) Flexbook. goo.gl/vOAnR

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• Published: 23 March 2024

Outcome differences by sex in oncology clinical trials

• Ashwin V. Kammula   ORCID: orcid.org/0000-0001-5284-721X 1 ,
• Alejandro A. Schäffer   ORCID: orcid.org/0000-0002-2147-8033 1 ,
• Padma Sheila Rajagopal 1 , 2 ,
• Razelle Kurzrock   ORCID: orcid.org/0000-0003-4110-1214 3 &
• Eytan Ruppin   ORCID: orcid.org/0000-0002-7862-3940 1  

Nature Communications volume  15 , Article number:  2608 ( 2024 ) Cite this article

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• Non-small-cell lung cancer
• Clinical trial design
• Targeted therapies

Identifying sex differences in outcomes and toxicity between males and females in oncology clinical trials is important and has also been mandated by National Institutes of Health policies. Here we analyze the Trialtrove database, finding that, strikingly, only 472/89,221 oncology clinical trials (0.5%) had curated post-treatment sex comparisons. Among 288 trials with comparisons of survival, outcome, or response, 16% report males having statistically significant better survival outcome or response, while 42% reported significantly better survival outcome or response for females. The strongest differences are in trials of EGFR inhibitors in lung cancer and rituximab in non-Hodgkin’s lymphoma (both favoring females). Among 44 trials with side effect comparisons, more trials report significantly lesser side effects in males ( N  = 22) than in females ( N  = 13). Thus, while statistical comparisons between sexes in oncology trials are rarely reported, important differences in outcome and toxicity exist. These considerable outcome and toxicity differences highlight the need for reporting sex differences more thoroughly going forward.

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Introduction

United States Public Health Service Act sec. 492B, 42 U.S.C. sec. 289a-2 states that “the Director of NIH [National Institutes of Health] shall ensure that the trial is designed and carried out in a manner sufficient to provide for a valid analysis of whether the variables being studied in the trial affect women or members of minority groups, as the case may be, differently than other subjects in the trial.” The importance of analyzing and comparing males and females was strengthened as of 2016 when NIH instituted a policy to require analysis of sex as a biological variable (SABV) in preclinical studies 1 . In Janine Clayton’s description of the pre-clinical policy implementation, the importance of following the law to compare males and females in clinical trials was re-emphasized 2 .

One reason that studying sex differences in clinical drug trials is important is because physiological and immunological differences between males and females may dictate distinctions in drug behavior 3 , 4 , 5 , 6 , 7 , 8 . For instance, body composition and metabolism differences between the sexes might influence pharmacokinetic and pharmacodynamics of drugs 9 . There are sex differences in incidence rates of many diseases including diabetes, cardiovascular diseases and cancer 5 , 6 , 7 , 8 , 10 , 11 . Furthermore, social constructs such as support systems might also show disparities by sex and could influence outcomes 12 .

However, disappointingly, the law and the policy are not widely followed 13 , 14 and there is a lack of understanding that the requirement is to compare by (biological) sex and not by (patient reported identity) gender 15 , 16 . For example, the latest in a series of medium-scale meta-analyses of NIH-funded trials, reported in a paper published in a leading journal in 2015, found that only 26/107 “reported at least one outcome by sex or explicitly included sex as a covariate in statistical analysis” 13 . To consider at least one outcome by sex as a positive is a weak standard and does not meet the legal requirement that there should be a comparison by sex. If a study does a “subgroup analysis” of males and females separately, that does not actually offer a comparison.

Designing, analyzing, and evaluating clinical trials to examine and compare treatments in both sexes requires careful planning. It is important to recruit sufficiently many males and females to have some power to detect differences 17 . Regulators must be clear and consistent about what sex-specific subgroup analyses and sex comparison analyses are expected in filings reporting trial results 18 . Differences in proper doses between males and females should be planned and should also consider the patient’s age and pharmacogenomic markers 4 , 8 , 11 , 19 . Differences in death rates and aging patterns should be considered when comparing survival characteristics of males and females, especially when studying diseases, such as cancer, that predominantly afflict older patients 20 , 21 , 22 , 23 .

Previous findings about differences in sex by survival, response and other outcomes are varied 14 , 24 , 25 , 26 . For example, there has been much debate about whether males have better outcomes in response to immunotherapy 27 , 28 , 29 , 30 , 31 , 32 , 33 . Others have hypothesized that females may have better survival in oncology clinical trials given their better survival in real-world data 10 , 20 , 34 , 35 . A very recent meta-analysis of oncology trials leading to drug approvals in the USA found no general significant differences in outcomes between males and females, even though individual trials may have significant differences 26 . In contrast, most studies and reviews on differences in side effects and toxicities by sex claim that females in oncology clinical trials experience more drug toxicities and other adverse events than males 3 , 36 , 37 , 38 , 39 , 40 , 41 . However, one recent large study reached the opposite conclusion 42 .

Given the complex and unclear state of sex disparities in clinical cancer research, the purpose of this study is to comprehensively characterize sex outcome comparisons in all oncology interventional clinical trials and to identify those comparisons that find a significant difference between males and females. We aimed to include all interventional oncology clinical trials and research the following questions:

What outcome comparisons by sex have been reported according to cancer type, treatment, and measurement (e.g., survival or side effects)?

What types of evidence for sex differences are reported and how often?

For any recurrent patterns of outcome differences that we find, are the patterns already known and generally accepted based on previous meta-analyses or other study designs?

While reaching the expected conclusion that few sex comparisons are done, can we find any technical barriers to increasing adherence to the law and suggest ways to possibly overcome them?

In this work, we report three main findings based on a systematic curation and evaluation strategy across all oncology clinical trials by leveraging a paid service called Trialtrove that collects and curates data from ClinicalTrials.gov and thousands of other sources into a semi-structured format 43 . First, direct statistical comparisons by sex in outcomes or side effects in clinical trials results papers are rare. Second, females demonstrate better survival outcomes and treatment responses in the majority of clinical trials that do perform sex-specific statistical comparisons and identify a difference. Finally, we find that that there are marked sex-specific differences for particular treatments, namely epidermal growth factor receptor (EGFR) inhibitors in non-small cell lung cancer (NSCLC) and rituximab in non-Hodgkin’s lymphoma (NHL), that extend beyond the overall underlying sex differences in survival in these two malignancies.

Search, curation and comparison to ClinicalTrials.gov

We queried the 89,221 oncology clinical trials in Trialtrove as of December 23, 2022. The ~60% of trials with exact accrual data show an aggregate total of more than 6 million patients in our analysis. Subsequent curation found at least one sex comparison reported between males and females in 472 trials (0.5%). The general plan of curation and analysis is summarized in (Fig.  1a ). Within this set of 472 trials, we identified 532 different post-treatment comparisons. 356 (66.9%) of these comparisons showed differences between males and females, and 176 (33.1%) showed similarities (Supplementary Table  1 ). Each comparison was labeled as either being in survival, outcome, or response (SOR) or in a post-treatment side effect (SE) and was classified by strength of evidence provided (Fig.  1b ).

a Schematic of our study design. Subpanel 1) Using text mining methods we identified trials in Trialtrove that may have a comparison of outcomes or side-effects by sex. Subpanel 2) We individually curated each candidate trial to record what was measured such as a survival difference or a difference in the frequency of a side effect such as rashes; we recorded whether there were differences or similarities between the sexes and what evidence was provided. b A flow diagram showing the breakdown of data collected. All data points originated from the 89,221 oncology clinical trials in Trialtrove on December 23, 2022. On the left we show all 532 sex comparisons found, among which 356 showed sex differences and 176 showed no difference. These comparison sets are classified by the type of evidence they present. Yellow boxes indicate categories with significant evidence. On the right we show another view of the data flow centered on the trials for which we found sex comparisons (shown on the left). Filtering for trials which present statistically significant evidence, we show the 288 trials with SOR comparisons and the 44 trials with SE comparisons. These two groups are broken down further by whether they show a preference towards males (blue), females (red), or neither (gray). Panel b may be interpreted as a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram in which the unit of analysis is a clinical trial.

In general, Trialtrove contains substantial information absent from ClinicalTrials.gov and we quantified this with one analysis. We evaluated on September 12, 2023 all 316 trials for which we found at least one sex comparison with a difference (not necessarily statistically significant). Among these 316 trials: 90 are not in ClinicalTrials.gov at all because they are in registries outside the USA, 142 are in ClinicalTrials.gov without any results, 79 mention sex only with respect to enrollment, which is called Participant Flow, and only 5 mention sex anywhere other than Participant Flow. Only 1 of these (NCT00418886) has separate analysis by sex. Ironically, the clearest indication that ClinicalTrials.gov does not record sex comparisons is trial NCT01274338 in which the associated paper entitled “Enhanced immune activation within the tumor microenvironment and circulation of female high-risk melanoma patients and improved survival with adjuvant CTLA4 blockade compared to males” 44 describes the sex comparison in the title; this paper is listed in the publications associated with trial NCT01274338. However, the Results subsection of the ClinicalTrials.gov entry for NCT01274338 has no analysis by sex. We recognize that ClinicalTrials.gov has neither the staff to do the curation nor the enforcement powers to require clinical trialists to deposit their results, so the Trialtrove curation adds considerable information.

Overall sex differences in Survival, Outcome, Response (SOR) across clinical trials

Overall, we found 288 trials with statistical analysis of SOR outcomes. This set contained 47 (16.3%) trials with significant evidence favoring males (25S, 11O, 11R), 122 (42.4%) trials with significant evidence favoring females (89S, 11O, 22R), and 119 (41.3%) trials with statistical evidence showing no difference between males and females (71S, 7O, 41R) (Fig.  2a ).

a Counts of trials that have sex comparisons of survival, outcome or response (SOR) performed either via multivariate or univariate analysis; b Counts for the subset of trials from panel a that use any of the 10 treatments that have the most sex comparisons, with ties broken arbitrarily. Color indicates the sex with better SOR; c Scatterplot of 81 treatments that have more than one trial with a sex comparison, such that the x value is the proportion of trials favoring females and the y value is the proportion of trials favoring males; the size of each circle is the number of found trials for that treatment; the color of each circle is the proportion of trials favoring males; trials with no SOR sex differences are not shown in panel c ; d Counts of trials from panel A according to the category of treatment given; treatment categories were ranked as 1. Immunotherapy, 2. Antibody, 3. Antibody-Drug Conjugate, 4. Targeted, 5. Chemo(therapy), 6. Immune-Other, 7. Other and 8. Supportive; a trial using multiple treatments of different categories was assigned to the lowest-number category of any treatment in the trial. In panels b and d , color indicates whether these trials show that males have better SOR (blue), females (pink), or that there is no difference (gray). Two-sided binomial tests were used to determine whether the proportions of male-favoring to female-favoring trials were significantly different from 1:1. P -values were corrected for multiple hypothesis testing using the Benjamini–Hochberg method. Significant FDR values are displayed in the figure. All others are available in Supplementary Tables. Source data are provided as a Source Data file.

We analyzed these 288 trials by the treatments used in each (Fig.  2b , Supplementary Data  1 ). Most drugs with more than one trial (54/81, 66.7%) showed improved SOR in females (Fig.  2c ). The three treatments most frequently assessed with sex-specific SOR analyses were erlotinib, gefitinib, and rituximab. For each of these treatments, we observed a statistically significant preference towards improved SOR in females relative to males (binomial test, FDR = 0.006, FDR = 0.011, FDR = 0.012 respectively).

We performed a binomial test for each category of treatment in our annotation (Methods). The Targeted (FDR = 8.5*10 −5 ), Chemotherapy (0.004), and Antibody (0.01) groups all showed significantly more trials with improved SOR in females than males (Fig.  2d , Supplementary Table  2 ).

Limited information about ages

Our primary analyses did not consider patient ages, although there are known interactions between sex and age affecting response to treatment 8 , 11 , 19 , 21 , 23 , 45 . Trialtrove coarsely annotates the ages eligible for a trial as any subset of three values, Children (ages 0–17), Adults (18–64), Older Adults (65–) and 71,832/89,221 trials have such an annotation. Among the oncology trials with an age annotation, the vast majority 59,792/71,832 (83%) have the age annotation “Adults; Older Adults”. Among the trials with a sex comparison and an age annotation, the proportion annotated as “Adults; Older Adults” is similar at 294/362 (81%). The proportion of all trials annotated as exclusively “Older Adults” is only 1261/59782 (2.1%), precluding any further analysis of significance into the differences between adults and older adults.

Quality control

Our analysis relies on Trialtrove curation, which could miss sex comparisons. Therefore, after doing most of our search curation, we selected 75 trials with large enrollments ≥200 including both males and females that appeared to have Trialtrove-curated results but did not have a sex comparison identified by our analysis (see Methods subsection entitled Quality Control of Query and Preliminary Results). These 75 trials are among the largest oncology trials done and hence are likely to have sufficient statistical power to detect a sex difference in outcomes if one exists. We intentionally did this quality control analysis shortly before finishing our curation so that if we found possible improvements in our methods, we could implement those improvements and we did implement one improvement.

To this end, we searched in detail any papers and abstracts published about those 75 large trials to see if any sex comparisons were missed by Trialtrove curators. The results of our quality control analysis are in Table  1 . The large majority of trials 65/75 (87%) of trials had a published paper by November 2022, so we could realistically assess whether the authors did an analysis by sex; for the other 10/75 (13%), the results in Trialtrove are based on conference abstracts or other brief communications. More than half the trials 38/75 (51%) had a paper with no analysis by sex confirming the concern that led to our study. For only 1 of 75 large trials we checked, there was a statistically significant sex comparison in the main document of the publication that Trialtrove curators missed. We infer that Trialtrove curators found the large majority of statistically significant published sex comparisons. As expected, there was a larger number of trials (8/75) that had an insignificant (7/75) or marginally significant (1/75, whether it is significant depends on not correcting for multiple tests) sex comparison relegated to the supplementary information; hence, Trialtrove curators missed the sex comparisons in those 8/75 publications. For the other 66/75 trials, no sex comparison was published. The most surprising and important finding is that a large proportion of papers (17/75, 23%) had a subgroup analysis by sex in which males and females were analyzed separately but no comparison was done. In the next paragraph and in the Discussion, we hypothesize as to why this practice of subgroup analysis of males and females separately with no sex comparison has arisen.

Subgroup analyses were always presented as a forest plot that included a row for the hazard ratio for males and a row for the hazard ratio for females; usually the hazard ratio compares two arms (e.g., new treatment vs. standard of care) for some form of survival or response. Most (14/17) of the papers that did separate analyses of males and females are published in one of four journals: Journal of Clinical Oncology (4), Lancet (3), Lancet Oncology (1), New England Journal of Medicine (6). In each of these journals the Editorial Office replots the forest plots for subgroup analysis in a nearly homogeneous format. One of the few inhomogeneities is that some of these forest plots include an accompanying direct comparison of male and female outcome, for example by a Cox regression test of survival, but most do not include such a comparison.

The observation that Trialtrove curation missed 8 comparisons that were not statistically significant and/or relegated to a supplement quantifies the curation bias against non-significant results and helps explain why we found so many fewer comparisons with similarities (176) than with differences (356) between sexes. Therefore, we focused most of our downstream analyses on the statistically significant comparisons. Since Trialtrove curation missed only one significant comparison in the main document, we suggest that our collection of significant comparisons is representative of the available comparisons.

At the suggestion of a reviewer, we added a second, larger assessment of all 147 trials that were eligible for our main analysis and were not found by us to have a sex comparison curated in Trialtrove, appeared to have results, and had enrollments in the slightly smaller range of [175, 199]. The enrollment criterion was selected to prefer large trials that have power to find sex differences while avoiding any overlap with the first assessment, which required enrollment ≥200. Encouragingly, we found only 2/147 trials with a significant SOR or side effect sex comparison that Trialtrove curators missed and 0/147 that our search methods missed (Table  1 ). The main differences in the outcomes of the two quality control assessments were an increase in the proportion of studies with a paper but no analysis by sex (51% in the first assessment and 65% in the second assessment) and a corresponding decrease (23% in the first assessment to 7% in the second assessment) in the studies with a paper that did a separate assessment of males and females. Possible reasons for this difference include that i) larger trials are more likely to have power to analyze males and females separately and ii) larger trials are more likely to be published in very high impact journals such as New England Journal of Medicine and Journal of Clinical Oncology, which have developed standardized, in-house figure designs for forest plots that are used to illustrate subgroup analyses, such as separate analyses of males and females. The trials with enrollment <200 are naturally less likely to be published in the highest impact journals and the lower impact journals do not necessarily encourage authors to do analyses by sex, either separately or in comparison (see Discussion).

Analysis of trials by starting year

To see how the use of sex comparisons has changed over time, we calculated the proportion of candidate trials that report sex-specific subgroup analyses as a function of the starting year of the trial. The proportion of trials with sex-specific comparisons decreased over time in trials of all phases, and this finding was consistent when trials were further split to Phase II and Phase III only (Supplementary Fig.  1 , Methods, Supplementary Table  3 ). The decline was especially substantial between trials with starting years in 2008–2009 (58/2030 trials had sex comparisons) and trials with starting years in 2016–2017 (20/1985 trials). This difference in proportions is highly significant ( P  < 4E-5, Fisher’s exact test, two-sided). We suggest that there are two overlapping reasons for this decline. First, as explained in the Discussion, in the first decade of the 21st century caution among biostatisticians about subgroup comparisons increased, especially regarding post hoc subgroup comparisons. Second, as we observed above, recent trial papers tend to report separate analysis of males and females as well as other subgroups but tend to avoid direct comparisons between subgroups. We provide more fine-grained data by single year and for each trial phase in Supplementary Data  2 . As an influential anecdotal example, we mention a meta-analysis of sex and response to immune checkpoint blockade; all 23 of the underlying trials analyzed males and females separately without a direct comparison 32 . Of note, we found the highest fraction of candidate trials with sex comparison in the Phase III set (Supplementary Fig.  1 ), suggesting that as drugs near requests for regulatory approval, doing comparisons by sex increases in importance.

It may be argued that the denominator of 89,221 is an overestimate for several reasons. Therefore, we counted the subset of trials that had an enrollment of more than 25, enrolled both males and females, had a known start year of 1993–2022, and had at least one data collection location in the United States including Puerto Rico. The last two requirements are because the United States Public Health Service Act sec. 492B, 42 U.S.C. sec. 289a-2 (quoted at the start of the Introduction) was enacted only in the United States in 1993. The number of trials in the numerator and denominator meeting the four requirements above are 215/17988 (1.2%). The numbers for each start year are shown in Supplementary Table  4 . To summarize the analysis over time, we do not observe any increase in the proportion of United States -based trials with a sex comparison after any of four key events: enactment in 1993 of the law mentioned above, establishment of ClinicalTrials.gov in 2000, requirement of trial registration in ClinicalTrials.gov starting around 2007, and implementation of the NIH policy on sex as a biological variable around 2016.

Two treatment/cancer type combinations drive favorable SOR outcomes for females: EGFR inhibitors in NSCLC and rituximab in NHL

We further analyzed trials with significant SOR comparisons by cancer type (Fig.  3a , Supplementary Table  5 ). NSCLC had the largest number of trials with systematic statistical SOR comparisons, 82 in total. There were significantly more trials with SOR that favored females ( N  = 35, 42.7%) than trials with SOR favoring males ( N  = 13, 15.9%) (FDR = 0.005). 34 (41.5%) trials showed no SOR difference between males and females.

a Counts of the trials from Fig.  2a by malignancy for the 10 malignancies with the most sex comparisons of SOR; b counts of NSCLC trials from a according to the treatment given; c Analysis of NSCLC SOR trials from a , split by those which used an EGFR inhibitor (EGFRi) and those which did not use an EGFR inhibitor (non-EGFRi). d Classification of NSCLC trials with statistical SOR comparisons by continent; trials on multiple continents were counted once for each continent; no continents have an over-representation of or under-representation of any continent among trials favoring females. e Classification of non-Hodgkin’s lymphoma (NHL) for the five most common treatments in trials with an SOR sex comparison. f Analysis of NHL SOR trials from a , split by those which used rituximab and those which used ‘Other’ non-rituximab treatments. In all panels, color indicates whether these trials show that males have better SOR (blue), females (pink), or that there is no difference (dark gray). Figures a , b and e used two-sided binomial tests to determine whether the proportion of male to female trials was significantly different from 1:1. P -values were corrected for multiple hypothesis testing using the Benjamini–Hochberg method. Figures c and f utilized two-sided hypergeometric tests to assess whether EGFR inhibitor trials or rituximab trials were enriched for improvement in females. Significant FDR values are displayed in the figure. All others are available in Supplementary Tables. Source Data are provided as a Source Data file.

We analyzed the NSCLC trials by the treatments used in the comparisons (Fig.  3b , Supplementary Table  6 ). We found that 36/82 NSCLC trials used epidermal growth factor receptor inhibitor (EGFRi) treatments, predominantly gefitinib and erlotinib. Of the EGFRi trials (Methods), strikingly, 21 (58.3%) had SOR favoring females, 1 (2.8%) favored males, and 14 (38.9%) observed no difference. In contrast, among all NSCLC trials involving other treatments, the emerging picture is more balanced, with 14 (30.4%) studies showing favorable SOR in females, and 12 (26.1%) studies favoring males (Fig.  3c ). The EGFR inhibitor trials have preferential SOR in females which goes beyond the overall finding that females have better SOR than males with NSCLC ( P  = 0.001, hypergeometric test, Methods). Given the hypotheses of potential sex-specific differences based on a higher frequency of somatic EGFR mutations among NSCLC tumors from non-smoking females in Asia, we evaluated sex-specific difference reporting by continent and did not observe a unique pattern relative to other continents (Fig.  3d , Supplementary Table  7 ).

In NHL, we identified 33 trials with significant SOR comparisons, 20 (60.6%) showing SOR that favored females, 4 (12.1%) with SOR favoring males, and 9 (27.3%) trials showing no SOR difference (Fig.  3e ). Notably, of 13 trials that used rituximab, 10 showed better SOR in females and only 1 in males, with 2 trials showing no significant difference (Fig.  3f , Supplementary Table  8 ). Given that rituximab is used in other non-cancer contexts, we sought to assess if there is a sex-specific association with rituximab across other types of trials. In Trialtrove, we identified 48 such trials, but including only 4 sex comparisons, reporting balanced results (Methods). Comparing the distributions of NHL trials that used rituximab to those which did not, the difference between the two is not significant ( P  = 0.30).

Acute myelogenous leukemia (AML) was the malignancy with the third largest difference between number of trials with SOR results favoring females and males, but not in a statistically significant manner (FDR = 0.2). These AML trials used a variety of different treatments, and we could not detect any related treatment specific patterns. Thus, AML may be a malignancy in which the better SOR in females manifests across different treatments and clinical trials, in difference from NSCLC and NHL

Analyses that would be interesting but cannot be done with Trialtrove data

The interaction effect between sex and age on morbidity and mortality is well established, with women living longer and experiencing greater frailty in older age. We are not able to observe the effect of this interaction with the available data 21 , 23 , 45 , 46 . Similarly, pharmacology studies have demonstrated that pharmacokinetics and pharmacodynamics are different between males and females 4 , 5 , 6 , 8 , 9 , 11 , 19 , 46 , with these factors as contributors for women experiencing more side effects (potentially older women more specifically). Regrettably, only 33 of the 472 trials in Trialtrove that performed sex-specific comparisons describe any data collection related to any of the cytochrome P450 genes involved in drug metabolism, precluding any systematic analysis of such interactions.

Males have fewer side effects than females

Overall, 97 (18.2%) of all comparisons we found were regarding post-treatment side effects (SE), often in trials of drugs intended to mitigate side effects. SE comparisons with multivariate or univariate evidence were performed in 44 trials. Those 44 include 22 trials (50%) that showed statistically significant lesser side effects in males, 13 (29.5%) in females, and 9 (20.5%) with no significant difference (Fig.  4a ). These trials are reported by treatment, treatment category, and cancer indication (Fig.  4b , Supplementary Tables  9 and 10 , Supplementary Data  3 ). Among these 44 trials, colorectal cancer is the most prevalent indication, with 15 trials overall, 10 (66.7%) favoring males (that is, lesser side effects in males), 3 (20.0%) favoring females, and 2 (13.3%) reporting no difference. Notably, 6 of these colorectal cancer trials used oxaliplatin, 5 (83.3%) favoring males and 1 (16.7%) favoring females (Supplementary Table  11 ).

a The numbers of trials with a multivariate or univariate comparison of side effects between males and females. lesser SE in Males means here that the side effect is statistically less common or less severe in males. b Counts of the trials in panel a for the five malignancies with the most comparisons - FDR for Colorectal cancer is not far from significant, P  = 0.07. For each malignancy, a two-sided binomial test was used to determine whether the proportion of male-favoring (blue) to female-favoring (pink) trials was significantly different from 1:1. P -values were corrected for multiple hypothesis testing using the Benjamini–Hochberg method. Significant FDR values are displayed in the figure. All others are available in Supplementary Tables. Source data are provided as a Source Data file.

We searched all oncology clinical trials in Trialtrove to identify trials that compared survival, outcome, or response (SOR) or side effects (SE) between males and females. This approach allowed our study to explore across cancer types and treatments to an extent that is orders of magnitude larger than prior studies. We reviewed the 89,221 oncology trials reported in Trialtrove between 1974 to 2022. We report three main findings: First, direct comparisons by sex in SOR or SE visible enough in clinical trial results papers to be curated by Trialtrove are rare—only 0.5% of studies included such a comparison. Second, females have better post-treatment SOR in most clinical trials that performed sex-specific comparisons. This difference is largely driven by trials in NSCLC, NHL, and AML rather than across all cancer types. Despite the better SOR outcomes for females versus males, though, males have significantly less toxicity than females in the subset of trials that could be analyzed for that parameter. Third, sex-specific differences are marked for particular therapies as applied to specific cancer types, namely EGFR inhibitors in NSCLC and rituximab in NHL, beyond the underlying overall sex differences observed in these two cancer types.

In response to our second research question regarding types of evidence, we found only 472 of 89,221 trials that reported performing curated sex-specific comparisons (a total of 532 such comparisons were done). This result is striking and discouraging given existing U.S. law and NIH goals. A common reason we observed is that clinical trials tend to report subgroup analyses, where males and females are analyzed separately, but not directly compared (Table  1 ). In clinical trial biostatistics, the common practice of analyzing males and females separately against the whole trial population can be improved by adding a Cox regression analysis, a statistical test of interaction, and/or a correction for testing of multiple subgroups 47 , to compare survival between males and females. Sun and colleagues describe an influential set of 11 subgroup analysis rules that should be considered in this context 48 . These rules are well-intentioned and appear to have improved data analysis practice over the past 10 years 49 . One of the rules is that the direction the subgroup effect should ideally be specified a priori. Our results support the need for a priori specification of subgroup testing by sex and potentially offer direction and estimation of subgroup-specific effects for clinical trial development. Another reason that we may not have observed sex-specific comparisons is that non-significant comparisons may not have been discussed or only shown in the supplementary information (which Trialtrove does not curate).

With regard to our third research question and identification of existing sex-specific patterns, the sex-specific association between EGFRi and NSCLC has been previously characterized in several studies 10 , 50 , 51 , 52 , 53 and has been attributed to a lower proportion of female smokers vs. male smokers and a higher proportion of female patients with EGFR somatic mutations vs. male patients 54 , 55 . EGFR mutations are known to be more prevalent in Asian countries and this epidemiological distinction is thought to explain why early trials of EGFR inhibitors, which did not consider EGFR mutation status, were more successful in Asia compared to other continents 54 . However, our geographic analysis of EGFRi trials does not support the hypothesis that the female advantage in EGFRi NSCLC trials rests on trials from Asia.

We also found that females with NHL have better SOR than males when treated with rituximab. Previous reports of improved SOR for females in NHL/rituximab were generated from single trials or anecdotal evidence collected from a few trials 56 , but the sex-preference in response to rituximab has not been systematically reported across trials. Importantly, other anecdotal reports of sex differences for various malignancy/treatment combinations 25 , 31 , 57 , 58 are not supported by our analysis of all oncology trials. Notably, rituximab does not show consistently better outcomes in females across other B-cell disorders 59 , suggesting that oncology clinical trials may reflect an interaction of NHL and rituximab with sex specificity, rather than a sex-specific pharmacodynamic property 60 . Further mechanistic studies are needed to learn if these differences are a result of sex hormone interactions with the drug, differing mutation frequencies between males and females, or other effects.

There are, of course, limitations to this work. This analysis relies on Trialtrove curation to capture reported sex differences from published papers. Careful, formal languages analysis (Methods) of the Trialtrove text avoided a frequent problem that (biological) sex is confused with (self-identified) gender 7 , 16 . We manually curated the Trialtrove results as well, but trials where sex comparison information was not reported in Trialtrove would not have been reviewed, except as part of our quality control assessments. As we noted, non-significant sex comparisons may be reported solely in the supplementary information. Indeed, the fact that we found 356 comparisons that found sex differences compared to 176 comparisons that found similarities, testifies to the known publication bias against reporting non-significant results.

Our analysis showcases the impact from and room for improvement in current policies to identify sex-specific results in clinical trials. A 2014 US Food and Drug Administration (FDA) Action Plan ( https://www.fda.gov/media/89307/download ) highlighted 27 actions divided into the priorities of “improving completeness/quality of demographic subgroup data collection, reporting, and analysis; identifying barriers to subgroup enrollment in clinical trials and employing strategies to encourage greater participation; and making demographic subgroup data more available and transparent.” Additionally, projects awarded by the FDA’s Office of Women’s Health Research will start to address some of the questions we bring up with this work, but this group has the funding for only a limited number of projects with a duration of 1–2 years per project ( https://www.fda.gov/science-research/womens-health-research/list-owh-research-program-awards-funding-year#2024 ). Prioritization cannot be at the level of the FDA alone. Incentives for recruiting sufficient patients and performing these comparisons must also be at the level of journals. As of 2016, several top-tier scientific and oncology-specific journals and journal families, including The Lancet family, Journal of the National Cancer Institute, the Cell family, the Nature family, and the Science family, have adopted SAGER guidelines that require reporting of sex/gender of participants and, to some extent, justification for inadequate powering for subgroup analysis 61 , 62 . SAGER guidelines or some similar alternative should be the norm across journals.

In conclusion, direct comparisons by sex in outcomes or side effects in papers reporting clinical trial results are still very rare. This is despite increasing interest in sex differences in clinical medicine and pharmacology 3 , 7 , 9 , 10 , 14 , 25 , 38 , 58 , 63 , 64 , the requirement of US law and its implementation by the NIH 2 , 32 , 65 , 66 , and interesting examples where different treatments by sex may lead to better collective outcomes 28 , 67 . Our findings of treatment-specific biases even in the current sparse comparisons supports the urgent need to perform sex comparisons on a much wider scale, which would likely reveal additional clinically important observations. Clinical trialists, biostatisticians and journal editors are in positions to highlight subgroup differences and to improve our understanding and leveraging of sex-specific treatment outcomes.

We systematically searched Trialtrove ( https://citeline.informa.com/trials/results ), an online repository of clinical trials to collect a set of trials that identified a difference in a post-treatment outcome between male and female patients. Trialtrove reports and summarizes each trial in 75 semi-structured data fields. We elected to query Trialtrove, rather than the more commonly used ClinicalTrials.gov, because Trialtrove has more consistent formatting that allows for use of formal language methods 68 .Trialtrove includes more treatment intervention trials than ClinicalTrials.gov because Trialtrove collects data including and beyond ClinicalTrials.gov 43 . The full Trialtrove data are available only under license, so we can only provide summary information 69 . Recent large studies that similarly used Trialtrove include a study predicting drug approvals, a study about the use of germline information in clinical trials, and a catalog of immunotherapy trials 69 , 70 , 71 .

Almost all results shown below are based on one consolidated search of a frozen and downloaded set of all 89,221 oncology trials present in the Trialtrove database on December 23, 2022. Data were processed during December 2022-March 2023. Usage of one earlier data freeze to explore search strategies and for quality control is described in the subsection below entitled Quality Control of Query and Preliminary Results. Trialtrove is updated every weekday and in our experience, new data in ClinicalTrials.gov appear in Trialtrove within days or a few weeks. To have a clear and consistent reference point, we had to take a single data freeze (in December 2022) to have a stable set of data to analyze.

Two data freezes

We searched in two major phases. The first phase (June–November 2022) was based on a Trialtrove data freeze in April 2022 and was intended to optimize our search methods. We initially expected that that any sex comparisons would be found in the column named ‘Trial Results’ but quality control revealed that many sex comparisons are instead in the field ‘Trial Notes’. Almost all results in this study are based on one consolidated search of a frozen and downloaded set of all 89,221 oncology trials present in the Trialtrove database on December 23, 2022.

Database filtering

To filter the 89,221 trials, we placed the following restrictions: the trial must treat patients of both sexes (the “Patient Gender” column must be equal to “Both”), the trial must have at least 25 patients enrolled or must not have specified the number of patients (the “Actual Accrual (No. of patients)” entry must be greater than or equal to 25 or must be blank), and the trial must have results (the “Trial Results”  or "Trial Notes" fields must contain of one of the below terms which suggest that results are reported).

[‘ORR’, ‘CR’, ‘DCR’, ‘RFS’, ‘OS’, ‘DFS’, ‘disease-free survival’, ‘LDFS’, ‘IDFS’, ‘PFS’, ‘progression-free survival’, ‘event-free survival’, ‘PFS4’, ‘FFP’, ‘Objective response’, ‘objective response’, ‘Complete control’, ‘complete control’, ‘Complete response’, ‘complete response’, ‘Overall response’, ‘overall response’, ‘Partial response’, ‘partial response’, ‘Disease control rate’, ‘disease control rate’, ‘Tumor response’, ‘tumor response’, ‘Survival rate’, ‘survival rate’, ‘Survival rates’, ‘survival rated’, ‘Response rate’, ‘response rate’, ‘Response rates’, ‘response rates’, ‘Remission rate’, ‘remission rate’, ‘Effective rate’, ‘effective rate’, ‘free survival was’, ‘pCR’, ‘PCR’, ‘mCR’, ‘nCR’, ‘cCR’, ‘QoL’, ‘pathological response’, ‘Pathological response’, ‘clinical response’, ‘Clinical response’, ‘cytogenetic response’, ‘Cytogenetic response’, ‘CCyR’, ‘hematological response’, ‘Hematological response’, ‘hematologic response’, ‘Hematologic response’, ‘CCR’, ‘recurrence rate’, ‘Recurrence rate’, ‘recurrence rates’, ‘Recurrence rates’, ‘CHR’, ‘cumulative response’, ‘distant metastasis-free survival’, ‘DMFS’, ‘durable clinical benefit rate’, ‘durable response rate’, ‘durable responses’, ‘Early molecular response’, ‘EMR’, ‘event-free survival’, ‘local failure-free survival’, ‘LFFS’, ‘major cytogenetic response’, ‘MCyR’, ‘major molecular response’, ‘MMR’, ‘mean duration of the response’, ‘Mean duration of the response’, ‘median treatment duration’, ‘Median treatment duration’, ‘median follow up’, ‘median followup’, ‘molecular response’, ‘MR’, ‘PCR rate’, ‘radiological response’, ‘regional failure-free survival’, ‘RFFS’, ‘resection rate’, ‘Resection rate’]

Identifying trials with sex comparisons

After this initial filtering, we narrowed the trial list to those in which we believed a comparison between males and females was reported in either Trial Results or Trial Notes. From the Python3 package re (v2.2.1), we used the findall() method with a regular expression of ‘\w+’ to split the Trial Results or Trial Notes field into an array of tokens where each token represented a word in the associated context. We required that one of these fields must contain a term regarding sex (“male”, “female”, “men”, “women”, “males”, “females”, “m”, “f”, “gender”, “sex”) within nine tokens of either:

A term suggesting a comparison: ‘more’, ‘less’, ‘fewer’, ‘greater’, ‘higher’, ‘lower’, ‘frequent’, ‘frequently’, ‘preferential’, ‘preferentially’, ‘associated’, ‘similar’, ‘similarly’, ‘better’, ‘compare’, ‘compared’, ‘difference’, ‘greater’, ‘longer’, ‘odds’, ‘rate’, ‘response’, ‘responses’, ‘shorter’, ‘significant’, ‘significantly’, ‘statistical’, ‘statistically’, ‘versus’, ‘vs’, ‘worse’, or ‘worst’

A term that may represent an outcome or side effect. (terms listed in the section Database Filtering)

The steps up to this point identified 11,259 candidate contexts over 4061 trials. Many of these contexts were detected as false positives in a semi-automatic manner due to extraneous uses of the terms ‘m’ and ‘f’ representing something other than ‘male’ and ‘female’ (see the subsection entitled Examples of Why ‘M’ and ‘F’ Usually Do Not Represent ‘Male’ and ‘Female’). Duplicate trials were removed. The remaining trials were manually curated as described in the next subsection, including checking original papers and abstracts if the Trialtrove annotations were ambiguous or incomplete.

Curation and annotation

In the curation process, we classified each comparison as either being in survival, outcome, or response (SOR) or in a post-treatment side effect (SE). Those labels were determined in our curation based on the text involved in the comparison: The label ‘Survival’ represents comparisons which explicitly state survival. The ‘Response’ label stands for response outcomes reported via the RECIST criteria as well as measurements of time to progression and relapse. The label ‘Outcomes’ refers to other reports of outcome, such as improvements in quality of life, that are not a formal measurement of survival or of response. The ‘SE’ label refers to any post-treatment toxicity or side effect regardless of severity, including nausea and vomiting, anemia neutropenia, and rashes, among others. Each sex comparison was additionally annotated for the type of evidence provided in one of four ranked categories: Multivariate Analysis Significant, Univariate Analysis Significant, Other Numerical Comparison, No Numerical Comparison. Most downstream analyses were focused on the 288 SOR and 44 SE trials that presented statistically significant differences or similarities with statistical tests. Due to a small number of trials analyzing side effects by sex, we did not subset the side effect category by severity or type.

Following automated trial filtering, each trial was manually annotated by both A.V.K and A.A.S. To aid our manual annotation, we automatically stored the textual context with nine words on either side flanking the sex term. We looked beyond the context to more of the Trial Results or Trial Notes field if the context was not sufficiently clear. We also looked in the original sources cited in Trialtrove as needed to clarify ambiguities in the Trialtrove curation. In our annotation, we aimed to answer the following questions:

Is there a true comparison being made between males and females?

Is there is a difference or no difference between males and females?

Could the comparison have been done before treatment (even if it was done later)?

What patient measurement (e.g., survival) is being compared?

What is the evidence type used in the comparison?

If there is a difference, does the difference represent an improvement in males or females?

In what disease(s) was this comparison made?

Which treatments used (among the Primary Tested Drugs field) led to this comparison being made?

When annotating comparisons by type (Question 4), we place comparisons into one of four categories: Difference in Survival (comparisons that explicitly state survival, such as overall survival, progression-free survival and event-free survival), Difference in Response (as reported by RECIST criteria or other measurements of progression and relapse), Difference in Other Outcome (all other reports of outcome such as Health Related Quality of Life (HRQOL), presence of brain metastases, and relative risk of death), Difference in Side Effect (any post-treatment side effect or toxicity). Additionally, when annotating comparisons for evidence type used, the possible options are Multivariate Analysis, Univariate Analysis, Other Numerical Comparison, No Numerical Comparison. Using these categories, a trial has a comparison that is not different (shortened to no diff. in the Figures) if either:

The evidence type is Multivariate Analysis or Univariate Analysis and the comparison was not statistically significant or

The evidence type is Other Numerical Comparison or No Numerical Comparison and the curation described the comparison with an adjective such as “same” or “similar” or “nearly identical”.

Examples of why ‘M’ and ‘F’ usually do not represent ‘male” and ‘female’

When looking for terms that may represent one sex or the other, we included the single letter abbreviations ‘M’ and ‘F’ in either upper case or lower case. These initials do represent ‘male’ and ‘female’ in a miniscule percentage of Trialtrove entries, but usually they represent something else.

M or m can appear in author initial, short for “months”, short for “meter”, in M.D., and in “m protein” as an initial for a treatment such as “methotrexate”; F or f can appear in author initial as an abbreviation for a treatment such as “fluorouracil” and as part of an html (hypertext markup language) string used in Trialtrove syntax, the sixth arm in a trial with at least six arms a, b, c, d, e, f.

Removal of candidate trials with duplicated information

To avoid double counting, we aimed to remove Trialtrove entries with overlapping results representing the same clinical trial or an umbrella trial including various sub-trials that have their own Trialtrove entries. Two trials were defined to be duplicates if the context of each (sex term, comparison term/results term) pair in one trial was contained in the contexts of the other trial. If the two trials had identical context sets, then the trial with the lower Trialtrove ID was removed. If one trial’s context set was a subset of another, then the smaller context trial was removed.

Statistics & reproducibility

Statistical tests were performed using the Python3 packages SciPy (v1.7.3) and statsmodels (v0.13.2). We performed subsequent analyses using Python3 and Pandas. We split our comparison set into a set of comparisons in survival, outcome, or response (SOR) and a set of comparisons in side effects (SE) and only considered comparisons with either multivariate or univariate significant evidence. For downstream analyses, rather than analyzing by comparisons, we analyzed by trials. This distinction is important because a trial may contain more than one comparison. Within the SOR comparison set, trials were classified based on the comparison with comparison type highest in this ranking; Survival > Response > Outcome.

Downstream analyses included classification according to the Disease field and the drugs tested. For one set of analyses combining different treatments, we (A. A. S. and S. P. R) classified treatments into the following mutually exclusive, ranked categories:

Immunotherapy (immune checkpoint blockade, 10)

Antibody (that are not immunotherapy, 22)

Antibody drug conjugate (3)

Targeted (71)

Chemotherapy (90)

Immune-Other (31)

Supportive (28)

The classification was done by expert knowledge and by using a published table of drugs to distinguish Targeted from Chemotherapy 72 . A trial was assigned to its highest ranked treatment category for the purpose of analyzing trials by these eight categories.

For both the SOR and SE comparisons, we counted the number of male, female, and same trials for different treatments, treatment categories, and cancer types. To determine whether a specific subset of trials was enriched for trials favoring males or females, we performed a two-sided binomial test of the hypothesis that the male favored to female favored trials are in a 1:1 proportion. Two-sided binomial tests were only performed if the number of male trials plus the number of female trials exceeded 3. Comparisons of three or fewer trials were deemed to have insufficient statistical power to draw meaningful conclusions. For each set of analyses where we divided the trial set by a specific variable (treatment, treatment category, cancer type), we used the Benjamini–Hochberg method to correct for multiple hypothesis testing and to calculate the false discovery rate (FDR). Binomial tests were performed using the binom.pmf() function from scipy.stats. FDR corrections were performed using the multitest.fdrcorrection() function from statsmodels.stats. Any analysis with FDR corrected p -value ≤ 0.05 was considered significant.

The above analysis was additionally performed by subsetting the non-small cell lung cancer trials and non-Hodgkin’s lymphoma trials by treatment.

To determine whether non-small cell lung cancer trials using EGFR inhibitors (afatinib, cetuximab, erlotinib, gefitinib, vandetanib) had significantly more female improved trials than NSCLC trials not using EGFR inhibitors, a hypergeometric test was performed, using the hypergeom() function from scipy.stats. The same analysis was performed to determine whether non-Hodgkin’s lymphoma trials using rituximab had significantly more female improved trials than NHL trials not using rituximab.

The principal sample size was determined by all available trials in Trialtrove that have curated sex comparisons. No trials were excluded. For analyses of trials of specific diseases (such as NSCLC) or specific treatments (such as rituximab) all oncology trials meeting those criteria were included. Since all applicable oncology clinical trials were included, no statistical method was used to predetermine the sample sizes. No data were excluded from the analyses. There was no randomization and no blinding. Curation of the trials in the Source data was done by A.V.K. and A.A.S who checked each other’s work to arrive at a consensus curation for each trial.

Quality control of query and preliminary results

In November 2022, using the April 2022 data download, we selected 75 trials that i) were not found to have a sex comparison, ii) appeared to have results based on the Trial Results field and iii) appeared to have enrolled at least 200 patients and patients of both sexes, making it likely that there was some power to detect a sex difference. We used the 75 trials selected in November 2022 to assess what our initial strategy may have missed and whether we should start over with a new data freeze. We found several trials with sex comparisons had been added to Trialtrove between April and November 2022. This finding led us to take the second data freeze and to run one consolidated query on that. Additionally, we systematically assessed any papers reporting results on these 75 selected trials to identify gaps in our search strategy and to assess the robustness of our approach. The quality control revealed one syntactic structure we had missed until November that detects some sex comparisons that showed no difference between males and females. We also found that trial results were sometimes misplaced in the Trial Notes column instead of the expected Trial Results column. These gaps were filled in our final query.

While revising the study, we did a second, similar quality control assessment using all 147 trials with an enrollment [175, 199] selected from the December 23, 2022 data freeze that appeared to have Results. This assessment was completed on September 23, 2023 and did take into consideration any papers about the 147 trials that had been published after the December 23, 2022 data freeze. This assessment did not reveal any sex comparisons that were in Trialtrove but missed by our analysis, so there was no need to revise our analysis methods.

Analysis of trends over time

To assess the proportion of trials that have a sex comparison as a function of time, we classified each trial according to the year of its start date, which is a structured field in Trialtrove. For sets of consecutive years, we computed the ratio of trials that have sex comparisons according to our curation (numerator) divided by the number of trials that passed our first layer of filters to be candidates to have a sex comparison (denominator). We also partitioned the trials by phase to assess whether trials of different phases had a higher or lower rate of sex comparisons. Only phases II and III had enough sex comparison trials to do a meaningful analysis.

Computational tools

All automated steps were performed using Python3 (v3.8 or v3.9). For data organization and analysis, the Python package Pandas (v1.4.2) was used. Figures were built using GraphPad Prism (v9.5.1), Matplotlib (v3.5.1), Adobe Photoshop (v13.0), Adobe Illustrator (v28.2), and BioRender.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The raw data from Trialtrove are available under restricted access only to license holders of Trialtrove via https://citeline.informa.com/trials/results . The processed data for all figure panels that have numerical data are available in the Source Data file. Additional processed data generated in this study are available in the Supplementary Tables and the Supplementary Data files.  Source data are provided with this paper.

Code availability

The most important Python programs used in this study are available via https://github.com/ruppinlab/ProcessTrialtrove and are also available at Zenodo via https://doi.org/10.5281/zenodo.10713794 73 . The programs can be run only if one has a Trialtrove license and can download trial data. Other readers may find the programs useful to read to understand in detail how we processed the Trialtrove data.

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Acknowledgements

This research was supported in part by the Intramural Research Program of the NIH, NCI (ER). This research was supported in part by NIH grants 5U01CA180888-08 and 5UG1CA233198-05 (RK). This work utilized the computational resources of the NIH HPC Biowulf cluster. ( http://hpc.nih.gov ).

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Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892, USA

Ashwin V. Kammula, Alejandro A. Schäffer, Padma Sheila Rajagopal & Eytan Ruppin

Women’s Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892, USA

Padma Sheila Rajagopal

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Conceptualization of the study: E.R. Data collection: A.V.K. and A.A.S. Data curation: A.V.K., A.A.S., P.S.R with guidance from R.K. and E.R. Software development: A.V.K,, supervised by A.A.S. Selection of methods for data analysis: A.V.K., A.A.S, P.S.R., R.K., and E.R. Literature search of related work: A.A.S. with guidance from P.S.R. and R.K. Visualization: A.V.K. Writing first draft: A.V.K., A.A.S, and P.S.R. Writing later drafts and editing: A.V.K., A.A.S., P.S.R., R.K., and E.R. Supervised the study: A.A.S. and E.R.

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Correspondence to Alejandro A. Schäffer or Eytan Ruppin .

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R.K. has received research funding from Boehringer Ingelheim, Debiopharm, Foundation Medicine, Genentech, Grifols, Guardant, Incyte, Konica Minolta, Medimmune, Merck Serono, Omniseq, Pfizer, Sequenom, Takeda, and TopAlliance and from the NCI; as well as consultant and/or speaker fees and/or advisory board/consultant for Actuate Therapeutics, AstraZeneca, Bicara Therapeutics, Inc., Biological Dynamics, Caris, Datar Cancer Genetics, Daiichi, EISAI, EOM Pharmaceuticals, Iylon, LabCorp, Merck, NeoGenomics, Neomed, Pfizer, Prosperdtx, Regeneron, Roche, TD2/Volastra, Turning Point Therapeutics, X-Biotech; has an equity interest in CureMatch Inc. and IDbyDNA; serves on the Board of CureMatch and CureMetrix, and is a co-founder of CureMatch. E.R. is a co-founder of Medaware Ltd, Metabomed Ltd and of Pangea Biomed, Ltd (divested from the latter). E.R. serves as a non-paid scientific consultant to Pangea Biomed, Ltd. The other authors declare that they have no potential competing interests.

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Kammula, A.V., Schäffer, A.A., Rajagopal, P.S. et al. Outcome differences by sex in oncology clinical trials. Nat Commun 15 , 2608 (2024). https://doi.org/10.1038/s41467-024-46945-x

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Once-Weekly Semaglutide in Adults with Overweight or Obesity

Collaborators.

• STEP 1 Study Group : Marianela Aguirre Ackermann ,  Cecilia Luquez ,  Marcos Mayer ,  Carla Musso ,  Susana Salzberg ,  Ides Colin ,  Ann Mertens ,  André Scheen ,  Jean-Paul Thissen ,  Luc Van Gaal ,  Zhivka Asyova ,  Anna-Maria Borissova ,  Nickolay Botushanov ,  Ivona Daskalova ,  Zdravko Kamenov ,  Adam Blackman ,  Martin D'Amours ,  Isabelle Labonte ,  Stephanie Li ,  Edmonton Edmonton ,  Derek Lowe ,  Sean Wharton ,  Sanaz Zarinehbaf-Asadi ,  Bjørn Richelsen ,  Kirsi Pietiläinen ,  Markku Savolainen ,  Sebastien Czernichow ,  Emmanuel Disse ,  Pierre Benite ,  Kamel Mohammedi ,  Arnaud Monier ,  Christine Poitou-Bernert ,  Pierre Serusclat ,  Jean-Francois Thuan ,  Christel Contzen ,  Moritz Mauro Erlinger ,  Michael Esser ,  Thomas Linn ,  Jörg Lüdemann ,  Karsten Milek ,  Nicoletta Nalazek ,  Andrea Rinke ,  Joachim Sauter ,  Thomas Schürholz ,  Alexander Segner ,  Liana Vismane ,  Ulrich Wendisch ,  Syamasis Bandyopadhyay ,  Dipti Chand ,  Piyush Desai ,  Vaishali Deshmukh ,  Yashdeep Gupta ,  P K Jabbar ,  Dinesh Jain ,  K Neelaveni ,  Shriraam Mahadevan ,  Rajesh Rajput ,  Sudhakar Reddy ,  Kongara Srikanth ,  A Unnikrishnan ,  Satoshi Inoue ,  Arihiro Kiyosue ,  Osamu Matsuoka ,  Hiraku Ono ,  Masamichi Yamada ,  Diego Espinoza Peralta ,  Silvia Jimenez-Ramos ,  Carlos Medina Pech ,  Pawel Bogdanski ,  Malgorzata Jozefowska ,  Agata Leksycka ,  Jaroslaw Ogonowski ,  Diana Alpenidze ,  Olga Ershova ,  Marina Kharakhulakh ,  Vadim Klimontov ,  Ludmila Ruyatkina ,  Marina Sergeeva-Kondrachenko ,  Ekaterina Troshina ,  Elena Zhdanova ,  Kuo-Chin Huang ,  Rachel Batterham ,  Matt Capehorn ,  Rhodri King ,  Michael Lean ,  Barbara McGowan ,  Khin Swe Myint ,  Adrian Park ,  Harpal Randeva ,  Georgina Russell ,  John Wilding ,  Hanid Audish ,  Darlene Bartilucci ,  Harold Bays ,  Ronald Brazg ,  Robert Broker ,  Kevin Cannon ,  Tira Chaicha-Brom ,  Matthew Davis ,  H Jackson Downey ,  Stephen Fehnel ,  Almena Free ,  Amina Haggag ,  Mitzie Hewitt ,  Priscilla Hollander ,  Misal Khan ,  Karen Laufer ,  Robert McNeill ,  John Nardandrea Jr ,  Lisa Neff ,  Kevin Niswender ,  Patrick O'Neil ,  John Pullman ,  Marina Raikhel ,  Scott Redrick ,  John Reed 3rd ,  Michele Reynolds ,  Luis Rivera-Colon ,  Julio Rosenstock ,  Erich Schramm ,  John Scott ,  Stephanie Shaw ,  Vijay Shivaswamy ,  Timothy Smith ,  Joseph Soufer ,  Stephen Straubing ,  Danny Sugimoto ,  Phillip Toth ,  Ralph Wade ,  Holly Wyatt ,  Lan Wynne

Affiliation

• 1 From the Department of Cardiovascular and Metabolic Medicine, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool (J.P.H.W.), University College London Centre for Obesity Research, Division of Medicine, University College London (R.L.B.), the National Institute of Health Research, UCLH Biomedical Research Centre (R.L.B.), the Centre for Weight Management and Metabolic Surgery, University College London Hospital (R.L.B.), and the Department of Diabetes and Endocrinology, Guy's and St. Thomas' NHS Foundation Trust (B.M.M.), London, and the Diabetes Research Centre, University of Leicester (M.D.) and the NIHR Leicester Biomedical Research Centre (M.D.), Leicester - all in the United Kingdom; Novo Nordisk, Søborg, Denmark (S.C., M.T.D.T., N.Z.); the Department of Endocrinology, Diabetology, and Metabolism, Antwerp University Hospital, University of Antwerp, Antwerp, Belgium (L.F.V.G.); the Departments of Internal Medicine/Endocrinology and Population and Data Sciences, University of Texas Southwestern Medical Center (I.L.), and the Dallas Diabetes Research Center at Medical City (J.R.) - both in Dallas; the Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia (T.A.W.); York University, McMaster University and Wharton Weight Management Clinic, Toronto (S.W.); the Department of Endocrinology, Hematology, and Gerontology, Graduate School of Medicine, Chiba University and Department of Diabetes, Metabolism, and Endocrinology, Chiba University Hospital, Chiba, Japan (K.Y.); and the Division of Endocrinology, Feinberg School of Medicine, Northwestern University, Chicago (R.F.K.).
• PMID: 33567185
• DOI: 10.1056/NEJMoa2032183

Background: Obesity is a global health challenge with few pharmacologic options. Whether adults with obesity can achieve weight loss with once-weekly semaglutide at a dose of 2.4 mg as an adjunct to lifestyle intervention has not been confirmed.

Methods: In this double-blind trial, we enrolled 1961 adults with a body-mass index (the weight in kilograms divided by the square of the height in meters) of 30 or greater (≥27 in persons with ≥1 weight-related coexisting condition), who did not have diabetes, and randomly assigned them, in a 2:1 ratio, to 68 weeks of treatment with once-weekly subcutaneous semaglutide (at a dose of 2.4 mg) or placebo, plus lifestyle intervention. The coprimary end points were the percentage change in body weight and weight reduction of at least 5%. The primary estimand (a precise description of the treatment effect reflecting the objective of the clinical trial) assessed effects regardless of treatment discontinuation or rescue interventions.

Results: The mean change in body weight from baseline to week 68 was -14.9% in the semaglutide group as compared with -2.4% with placebo, for an estimated treatment difference of -12.4 percentage points (95% confidence interval [CI], -13.4 to -11.5; P<0.001). More participants in the semaglutide group than in the placebo group achieved weight reductions of 5% or more (1047 participants [86.4%] vs. 182 [31.5%]), 10% or more (838 [69.1%] vs. 69 [12.0%]), and 15% or more (612 [50.5%] vs. 28 [4.9%]) at week 68 (P<0.001 for all three comparisons of odds). The change in body weight from baseline to week 68 was -15.3 kg in the semaglutide group as compared with -2.6 kg in the placebo group (estimated treatment difference, -12.7 kg; 95% CI, -13.7 to -11.7). Participants who received semaglutide had a greater improvement with respect to cardiometabolic risk factors and a greater increase in participant-reported physical functioning from baseline than those who received placebo. Nausea and diarrhea were the most common adverse events with semaglutide; they were typically transient and mild-to-moderate in severity and subsided with time. More participants in the semaglutide group than in the placebo group discontinued treatment owing to gastrointestinal events (59 [4.5%] vs. 5 [0.8%]).

Conclusions: In participants with overweight or obesity, 2.4 mg of semaglutide once weekly plus lifestyle intervention was associated with sustained, clinically relevant reduction in body weight. (Funded by Novo Nordisk; STEP 1 ClinicalTrials.gov number, NCT03548935 ).

Copyright © 2021 Massachusetts Medical Society.

Publication types

• Multicenter Study
• Randomized Controlled Trial
• Research Support, Non-U.S. Gov't
• Anti-Obesity Agents / administration & dosage*
• Anti-Obesity Agents / adverse effects
• Body Composition / drug effects
• Body Mass Index
• Cholelithiasis / chemically induced
• Diarrhea / chemically induced
• Double-Blind Method
• Glucagon-Like Peptide 1 / agonists*
• Glucagon-Like Peptides / administration & dosage*
• Glucagon-Like Peptides / adverse effects
• Healthy Lifestyle
• Injections, Subcutaneous
• Lipids / blood
• Middle Aged
• Nausea / chemically induced
• Obesity / complications
• Obesity / drug therapy*
• Prediabetic State / complications
• Weight Loss / drug effects
• Anti-Obesity Agents
• semaglutide
• Glucagon-Like Peptides
• Glucagon-Like Peptide 1

Associated data

• ClinicalTrials.gov/NCT03548935

Collaborative University of Cincinnati Cancer Center team opens Phase 2 brain tumor trial

Falk catalyst award funds next phase of research seeking new treatments for glioblastomas.

A multidisciplinary team of University of Cincinnati Cancer Center researchers have opened a Phase 2 clinical trial to test a new combination treatment for glioblastomas (GBM), the most deadly form of brain tumors. 

The team, led by UC’s Pankaj Desai, PhD, and Trisha Wise-Draper, MD, PhD, has been awarded a Catalyst Research Award from the Dr. Ralph and Marian Falk Medical Research Trust to move the trial forward.

Study background

Difficult to diagnose at early stages, GBMs are aggressive brain tumors that become symptomatic once the tumor is substantial. Current treatments include immediate surgery to safely remove as much tumor as possible, radiation and chemotherapy, but the tumor often recurs or becomes resistant to treatments. The average patient survives no more than 15 months after diagnosis. 

Drug-based treatments for GBMs face an additional challenge known as the blood-brain barrier, which only allows certain compounds into the brain based on their physical and chemical properties.  

The research team is focused on the use of a drug called letrozole that has been used for more than 20 years as a treatment for breast cancer. The drug targets an enzyme called aromatase that is present in the breast cancer cells and helps the cells grow. 

Early research in Desai’s lab found that aromatase was present in brain tumor cells, making letrozole a potential new treatment for GBMs.

The team is testing the drug letrozole as a treatment for glioblastoma, a deadly and aggressive brain tumor. Photo/National Cancer Institute.

Phase 0/1 trial results

To bring letrozole from Desai’s lab to patients’ bedsides, he collaborated with Wise-Draper and neuro-oncologists and neurosurgeons at  UC’s Brain Tumor Center  to launch a Phase 0/1 clinical trial. 

“In the academic setting, we are very good at doing molecular research that enhances our  understanding of the mechanism of disease and preclinical characterization of efficacy, safety and other aspects of drug development research,” said Desai, professor and chair of the Pharmaceutical Sciences Division and director of the drug development graduate program in UC’s James L. Winkle College of Pharmacy. “But you can’t translate this into a clinical trial without a Phase 1 clinical trial expert like Dr. Wise-Draper and the experts at the Brain Tumor Center.” 

The researchers published the results of the Phase 0/1 trial March 26 in Clinical Cancer Research , a journal of the American Association for Cancer Research.

Pankaj Desai, PhD. Photo/Andrew Higley/UC Marketing + Brand.

“Letrozole was safe up to the highest dose, and there were no safety concerns in the Phase 0/1 trial,” said Wise-Draper, section head of Medical Oncology and professor in the Division of Hematology/Oncology in UC’s College of Medicine. “The biggest conclusion is that it was safe and that we could reach what we felt was going to be the effective dose based on Dr. Desai’s preclinical work.” 

The research team collected tumor tissues from patients enrolled in the Phase 0/1 trial and found that letrozole was crossing the blood-brain barrier when they analyzed the samples in Desai’s lab. 

“We can categorically show that in humans the drug actually crosses and reaches the brain tumor at concentrations that we believe are likely to be most efficacious,” Desai said. 

Phase 2 trial design

Trisha Wise-Draper, MD, PhD. Photo/Nyla Sauter/University of Cincinnati Cancer Center.

Since GBMs are aggressive and complicated tumors, Desai said most likely new effective treatments will be combinations of drugs instead of one single drug. 

In the Phase 2 trial, patients will be given letrozole in combination with a chemotherapy drug called temozolomide that is already approved as a GBM treatment. Desai said preclinical research in his lab and input from Brain Tumor Center collaborators, including neuro-oncologist and former UC faculty member Soma Sengupta, suggested this combination treatment could be more effective than letrozole alone. 

A total of 19 patients with recurrent GBM who are no longer eligible for additional surgery will be  enrolled in the first stage of the trial. The results from this trial will guide the design of future larger Phase 2 trials. 

The team estimates it will complete enrollment within two years, and two patients have already been enrolled. 

Collaboration and funding support

Wise-Draper and Desai have worked together on various research projects for nearly 15 years and said this project would not be moving forward without the varied expertise each team member brings. 

“I think collaboration with multidisciplinary teams is critical to be able to have the expertise and all the components you need, including biostatistics, pharmacokinetics, clinical, basic science and neuro-oncology expertise,” Wise-Draper said. “The future of all science is team science. No one really can do everything on their own anymore because we’re all too specialized.” 

“Only academic centers with integrated scientific and clinical expertise are able to move their molecules from the research bench to clinical trials,” Desai added. “It takes a lot of persistence, ups and downs, highs and lows of funding, but we have been supported by a very strong team of people. It’s a journey that has taken a while and a lot of hard work by a number of people, and we’re in a very exciting stage.”

Early-stage support for the preclinical and clinical trial studies was provided by the UC Brain Tumor Center, where investigators from UC’s colleges of Medicine, Pharmacy, Engineering and Applied Science and Cincinnati Children’s Hospital collaborate on brain tumor research. 

UC’s Brain Tumor Center provided direct support for the completion of the Phase 0/1 trial and some of the correlative mechanistic studies that will continue during the Phase 2 trials using funds raised in the annual Walk Ahead for a Brain Tumor Discoveries fundraiser. 

The Falk Catalyst Award provides up to $350,000 in seed funding to support translational research projects, which the researchers said was crucial in opening the new trial.  

“Oftentimes the funding is somewhat limited for initial clinical trial development compared to many other more early-stage studies that you can do,” Desai said. “So that gap is filled by foundations like the Falk Medical Research Trust, and that really is very helpful and plays a critical role in accelerating clinical development.”

“It would not be possible if we didn’t have the funding to be able to bring this combination into patients that desperately need new treatment options,” Wise-Draper said.  

As the clinical trial progresses, the team is also collaborating to find other drugs to combine with letrozole to treat GBMs, funded by a $1.19 million  National Institutes of Health/National Institute of Neurological Disorders and Stroke grant . The team is already preparing a proposal for larger confirmatory Phase 2 studies and expanding the opportunities for cutting-edge brain tumor clinical trials in Cincinnati.

Desai said the ongoing research includes additional collaboration from experts including David Plas, PhD, Biplab DasGupta, PhD, and Tim Phoenix, PhD (molecular/cancer biology); Gary Gudelsky, PhD (neuro-pharmacology) Rekha Chaudhary, MD, and Lalanthica Yogendran, MD (neuro-oncology); Mario Medvedovic, PhD (bioinformatics and genomics); and Shesh Rai, PhD (biostatistics). Many graduate students, postdoctoral fellows and the clinical trials support staff also provide essential support for the project.   

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The University of Cincinnati is leading public urban universities into a new era of innovation and impact. Our faculty, staff and students are saving lives, changing outcomes and bending the future in our city's direction.  Next Lives Here.

For more information on the trial, please call 513-584-7698 or email  [email protected] .

The team also recently published research in the International Journal of Molecular Sciences reviewing approaches to overcome treatment resistance to temozolomide to treat GBMs.

Featured photo at top of Drs. Desai and Wise-Draper. Photo/Nyla Sauter/University of Cincinnati Cancer Center. 

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eTable 1. Clinical Trial Characteristics and Prognostic Risk Score

eTable 2. Socioeconomic Status and Risk of Hospital Stay and Emergency Room Visit With Insurance Broken Out by Medicare Alone, Medicare+Private, Medicaid+Medicare

eTable 3. Socioeconomic Status and Risk of Hospital Stay and Emergency Room Visit With Clustering by Study ID Rather Than Cancer Type

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Hershman DL , Vaidya R , Till C, et al. Socioeconomic Deprivation and Health Care Use in Patients Enrolled in SWOG Cancer Clinical Trials. JAMA Netw Open. 2024;7(3):e244008. doi:10.1001/jamanetworkopen.2024.4008

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Socioeconomic Deprivation and Health Care Use in Patients Enrolled in SWOG Cancer Clinical Trials

• 1 Columbia University, New York, New York
• 2 SWOG Statistics and Data Management Center, Fred Hutchinson Cancer Research Center, Seattle, Washington

Question   What is the association between socioeconomic factors and emergency department (ED) visits and hospital stays (HS) among individuals enrolled in Medicare who participate in cancer clinical trials?

Findings   In this cohort study of 3027 patients aged 65 years or older who participated in a cancer clinical trial and had Medicare, 36.1% had an ED visit and 32.4% had an HS. Patients who lived in areas with the most socioeconomic deprivation had a 62% increase in risk of either an ED visit or HS; patients eligible for both Medicare and Medicaid were 96% more likely to have an ED visit.

Meaning   These findings suggest that despite participation in cancer clinical trials, older patients living in areas with higher socioeconomic deprivation and those who are dual eligible for Medicaid and Medicare, which is a marker of economic disadvantage, have an increased risk of unplanned emergency care use.

Importance   Reducing acute care use is an important strategy for improving value. Patients with cancer are at risk for unplanned emergency department (ED) visits and hospital stays (HS). Clinical trial patients have homogeneous treatment; despite this, structural barriers to care may independently impact acute care use.

Objective   To examine whether ED visits and HS within 12 months of trial enrollment are more common among Medicare enrollees who live in areas of socioeconomic deprivation or have Medicaid insurance.

Design, Setting, and Participants   This cohort study included patients with cancer who were 65 years or older and treated in SWOG Cancer Research Network trials from 1999 to 2018 using data linked to Medicare claims. Data were collected from 1999 to 2019 and analyzed from 2022 to 2024.

Main Outcomes and Measures   Outcomes were ED visits, HS, and costs in the first year following enrollment. Neighborhood socioeconomic deprivation was measured using patients’ zip code linked to the Area Deprivation Index (ADI), measured on a 0 to 100 scale for increasing deprivation and categorized into tertiles (T1 to T3). Type of insurance was classified as Medicare with or without commercial insurance vs dual Medicare and Medicaid. Demographic, clinical, and prognostic factors were captured from trial records. Multivariable regression was used, and the association of ADI and insurance with each outcome was considered separately.

Results   In total, 3027 trial participants were analyzed. The median (range) age was 71 (65-98) years, 1280 (32.3%) were female, 221 (7.3%) were Black patients, 2717 (89.8%) were White patients, 90 (3.0%) had Medicare and Medicaid insurance, and 660 (22.3%) were in the areas of highest deprivation (ADI-T3). In all, 1094 patients (36.1%) had an ED visit and 983 patients (32.4%) had an HS. In multivariable generalized estimating equation, patients living in areas categorized as ADI-T3 were more likely to have an ED visit (OR, 1.34; 95% CI, 1.10-1.62; P  = .004). A similar but nonsignificant pattern was observed for HS (OR, 1.36; 95% CI, 0.96-1.93; P  = .08). Patients from areas with the highest deprivation had a 62% increase in risk of either an ED visit or HS (OR, 1.62; 95% CI, 1.25-2.09; P  < .001). Patients with Medicare and Medicaid were 96% more likely to have an ED visit (OR, 1.96; 95% CI, 1.56-2.46; P  < .001).

Conclusions and Relevance   In this cohort of older patients enrolled in clinical trials, neighborhood deprivation and economic disadvantage were associated with an increase in ED visits and HS. Efforts are needed to ensure adequate resources to prevent unplanned use of acute care in socioeconomically vulnerable populations.

Patients with cancer from socioeconomically deprived areas (ie, geographical areas with a high proportion of people who are disadvantaged due to factors such as poverty, discrimination, or lack of access to basic necessities) have worse cancer outcomes. 1 , 2 This has been attributed to the fact that socioeconomically deprived areas have limited access to screening and treatment services, and patients living in these areas tend to have more advanced disease at presentation. 3 - 5 We have previously shown that even among patients enrolled in clinical trials with uniform treatment and after accounting for race, ethnicity, age, and insurance-related factors, patients from the most socioeconomically deprived areas had a greater risk of death and worse progression-free and cancer-specific survival. This suggests area-level deprivation and both cancer and noncancer outcomes may be associated independent of key patient-level sociodemographic factors. 5

Population-based studies suggest that patients with cancer who are socioeconomically vulnerable have higher rates of emergency health care use. 6 In a series evaluating 25 000 patients with advanced solid tumors from the California Cancer Registry, 71% of the patients were hospitalized in the year after diagnosis. Furthermore, the 67% of unplanned hospitalizations originated in the emergency department (ED). Race, ethnicity, insurance type, and socioeconomic status were all associated with hospital readmission rates. Reducing unplanned ED visits and hospital stays (HS) are an important strategy for improving the quality of care and reducing the cost of cancer care.

Studies have shown that the association between both area-level socioeconomic deprivation (ADI) and individual-level socioeconomic deprivation (as measured by insurance) and survival outcomes persist for patients treated in clinical trials. 5 , 7 Less is known about the risk of noncancer outcomes and complications resulting in unplanned acute care, such as ED visits and HS, which may provide an opportunity for an intervention strategy.

This cohort study follows the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline. Written informed consent for participants enrolled in each clinical protocol was previously obtained for all participants. Approval to conduct this research was obtained from the Institutional Review Board of Cancer Research and Biostatistics in Seattle, Washington.

We obtained data from the SWOG Cancer Research Network and included patients from clinical trials for 6 disease types (bladder, breast, colorectal, lung, prostate, and myeloma) (eTable 1 in Supplement 1 ). Trial records were linked to Medicare claims data according to social security number, sex, and date of birth. To be included, patients were required to be aged 65 years or older at time of enrollment and to have at least 12 months’ Medicare Parts A and B coverage with no concurrent HMO coverage.

Demographic variables, including age, sex, and self-reported race and ethnicity, were obtained at the time of enrollment. Race and ethnicity were assessed because they are known to be associated with increased health care use, as well as social determinants of health, and were collected prospectively by the clinical trials staff at each trial site. Potential differences in prognostic risk across the panel of different studies were accounted for using a study-specific prognostic risk score. For each study, we identified the key baseline clinical risk factors that were included as stratification variables in the trials. We then summed the number of adverse clinical risk factors, creating a composite risk score, standardized to a 0 to 100 scale, and split at the approximate median. 5

Neighborhood socioeconomic deprivation was measured using patients’ residential zip code linked to the area deprivation index (ADI), which was measured on a 0 to 100 scale. Higher ADI denotes areas of higher deprivation. Patients missing zip code are not included.

HS were defined using the MedPAR file, by specifying NCH claims codes 60 to 64, 71, or 82, or, if claims code was missing, by specifying Skilled Nursing Facility indicator was not missing and was not N. HS with an admission date occurring within 1 year after registration were included. HS with different MedPAR ID were considered unique, even if dates of stay between 2 HS were overlapping.

ED visits were identified using 2 data sources: (1) outpatient revenue center data, using revenue center codes 0450-0459 and 0981; and (2) the MedPAR file, when the ED charge amount field was nonmissing and nonzero. As with HS, all ED visits occurring within 1 year after registration were included, with the potential of multiple observations per person.

To analyze health care costs as an outcome, claims cost data were compiled from MedPAR, Home Health Agency, outpatient, carrier, hospice, and durable medical equipment databases. Overall costs were examined, as well as separately by Medicare, beneficiary, and primary payers within the first 12 months after registration. Costs were inflated to 2021 US dollars based on the Personal Consumption Expenditure price index.

To assess the potential for bias, baseline characteristics were compared between those included in this analysis and those aged 65 years or older from the same studies who were not included due to HMO membership or lack of social security number. Generalized estimating equations (GEE) with a logit link were used to examine the binary health care use outcomes, accounting for clustering by cancer type. Analyses were conducted separately for HS (1 or more vs 0) or ED visit (1 or more vs 0), as well as a combined outcome, HS or ED visit (yes vs no to both). Two independent estimators were explored: ADI and insurance type. ADI was categorized into tertiles based on overall US distribution; the first tertile, representing areas with the least deprivation, was used as the referent category. Based on previous results in nontrial cancer patients, which showed differences in clinical outcomes between Medicare patients with vs without commercial insurance, we classified type of insurance at trial enrollment as Medicare alone, Medicare and commercial, or Medicare and Medicaid. 8 As initial analyses showed similar use outcomes between patients with Medicare alone and Medicare and commercial insurance, for the primary insurance analysis, these groups were combined for increased power and to highlight the association of Medicaid insurance as an indicator of socioeconomic vulnerability. Both univariate and multivariate analyses were performed. Multivariable regression analyses included covariates for age (continuous), race (Black vs White vs other), study, and prognostic risk (above vs below the median). Given the limited number of patients with Medicaid insurance, no analysis of the interaction of insurance type and ADI was conducted. Instead, these variables were considered separately as area-level and individual-level measures of socioeconomic deprivation. We separately examined whether clustering at the study level rather than by cancer type meaningfully changed the findings.

Mean values of health use costs were found separately by insurance status, ADI tertile, and payer type. P values were calculated using linear mixed model regression with a log link under a gamma distribution for analyzing cost data, with cancer type as a random effect, adjusted for age, race, study, and baseline prognostic risk score. A 2-sided significance level P < .05 was chosen. The software package SAS version 9.4 (SAS Institute) was used for analyses. Data were collected from 1999 to 2019 (registrations ranged from 1999 to 2018, plus 1 year postregistration in 2019) and analyzed from 2022 to 2024.

In total, 3027 patients were analyzed. Median (IQR) age was 71 (65-98) years, 1280 (32.3%) were female, 221 (7.3%) were Black patients, and 2717 (89.8%) were White patients ( Table 1 ). Compared with the patients not included from the same trials, the included patients were more likely to be White individuals; to be not Hispanic individuals; to be in breast, myeloma, or prostate cancer studies; to be registered in 2004 or later; and to have a lower prognostic risk score. Additionally, 913 patients (30.2%) had Medicare alone, 90 (3.0%) had Medicare and Medicaid insurance, and 2024 (66.9%) had Medicare and commercial. There were no differences in outcomes between patients with Medicare alone vs Medicare and commercial insurance (eTable 2 in Supplement 1 ). Thus, these groups were combined for our primary insurance analysis. Additionally, 1344 patients (45.4%) were in the lowest category of ADI (T1), and 660 (22.3%) were in the highest (T3). In all, 983 patients (32.4%) experienced HS, representing a total of 1805 HS, and 1094 patients (36.1%) had ED visits, representing a total of 2084 ED visits; and 1349 (44.6%) had HS or an ED visit.

In multivariable GEE analysis, patients living in areas with the highest deprivation (ie, T3) were significantly more likely to experience an ED visit (OR, 1.34; 95% CI, 1.10-1.62; P  = .004) ( Table 2 ). A similar but nonsignificant association was seen with respect to HS (OR, 1.36; 95% CI, 0.96-1.93; P  = .08). Overall, there was 62% increase in risk of either ED visit or HS for patients from areas with the highest deprivation (OR, 1.62; 95% CI, 1.25-2.09; P  < .001).

In adjusted analyses, patients with Medicare and Medicaid insurance were more likely to have ED visit in the first year (OR, 1.96; 95% CI, 1.56-2.46; P  < .001; Table 2 ). In contrast, no increased risk of HS for Medicare and Medicaid patients was observed. The findings were similar when clustering was at the study level rather than cancer level (eTable 3 in Supplement 1 ).

Patients from areas with the highest deprivation had greater total mean (SD) costs than those from the most affluent areas ($46 070.55 [$46 769.98] vs $40 547.39 [$42 755.70]; P  < .001). Findings were similarly discrepant for costs paid by Medicare and costs paid by the patient ( Table 3 ). In contrast, mean total, Medicare, and patient costs trended higher for patients with Medicaid and Medicare compared with those with Medicare and commercial insurance, the differences were not statistically significant.

In this cohort study of older patients enrolled in clinical trials, ED visits and HS were frequent within the first year following clinical trial enrollment. Both neighborhood deprivation and economic disadvantage as measured by the presence of Medicaid insurance were associated with increased ED visits and HS within the first 12 months among patients with Medicare. Therefore, despite participation in cancer clinical trials, older patients living with higher social needs have an increased risk of unplanned emergency care use. Moreover, the observed association increased as area level deprivation progressed from most affluent to most socioeconomically vulnerable, improving confidence in the validity of the findings.

The rising cost of cancer care is a major public health issue, and with newer, more targeted therapies, the costs are likely to increase. Decreasing unplanned hospitalizations provides an opportunity to decrease costs. Ironically, we found that the total care costs, those paid by Medicare and those paid by the patient, were lowest among patients who lived in the most affluent areas. Previous reports suggest that more than two-thirds of unavoidable hospitalizations are the result of cancer-related symptoms. One proven solution to reduce unplanned hospitalizations among cancer patients is active symptom monitoring. 9 It is known that active symptom monitoring of patients undergoing chemotherapy has reduced health care use, improved quality of life, and increased survival. 10 , 11 In the community setting symptom monitoring with electronic patient reported outcomes decreased hospitalizations from 32% to 20%, emergency visits from 42% to 38% and reduced the total cost of care by an average of $1146 per member per month. 12

Several prior studies have reported an association between socioeconomic status and risk of hospital readmissions. A machine learning approach based on 13 000 cancer patients found that 30-day readmission was associated with neighborhood income, wealth index, crime index, home values, and comorbidity index. 13 Similarly, a study using the California Cancer Registry linked to inpatient discharge data found that rehospitalization was associated with Black race, Hispanic ethnicity, public insurance or no insurance, and lower socioeconomic status. Fewer studies have evaluated nonclinical factors associated with initial unplanned hospitalization. 14

There are several strengths to our study. Participants were prospectively enrolled and baseline data was collected on all patients. For each study, patients were required to adhere to uniform protocol-specific therapy. All of the patients in this cohort were treated uniformly. Uniform access to protocol therapy also limits the confounding influence of initial access to cancer care.

This study has limitations. Patients were required to be enrolled in Medicare to be included in this study, thus all analyzed patients were older than age 65 years. Given that older patients are often underrepresented in clinical trials, selection bias may limit the generalizability of the results. All SWOG Cancer Research Network studies mandated a Zubrod score of 0 to 2, specifying that patients needed to be at least ambulatory and capable of self-care, as part of the inclusion criteria. Thus, patients with severe complications may not have been captured, which could also limit the generalizability of our results. Additionally, the reason for the hospitalization or ED visit was not available, so it was not possible to know how many could have been avoided. Finally, because the multilevel modeling strategy aims to identify aggregate patterns across a diverse set of cancer types and trials, results at the individual cancer categories and trial levels are not readily interpretable.

In this cohort study of elderly patients enrolled in clinical trials, neighborhood deprivation and economic disadvantage were associated with an increase in ED visits and HS. As a result, where a patient receives their care can account for disparities in outcome, even among clinical trials participants. Identifying patients with the highest risk may be an helpful strategy for targeted interventions. Policies to mitigate socioeconomic differences in cancer outcomes should emphasize access to cancer care services during and beyond initial therapy. Substantial efforts to increase diversity in clinical trials participation are under way. 15 Efforts are needed to ensure adequate resources to prevent unplanned use of acute care in vulnerable populations.

Accepted for Publication: January 31, 2024.

Published: March 28, 2024. doi:10.1001/jamanetworkopen.2024.4008

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Hershman DL et al. JAMA Network Open .

Corresponding Author: Dawn L. Hershman, MD, Medicine and Epidemiology, Columbia University, 161 Ft Washington, Ste 1068, New York, NY 10032 ( [email protected] ).

Author Contributions: Drs Herchman and Unger had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Hershman, Vaidya, Ramsey, Unger.

Acquisition, analysis, or interpretation of data: Hershman, Vaidya, Till, Barlow, LeBlanc, Unger.

Drafting of the manuscript: Hershman, Unger.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Vaidya, Till, Ramsey, Unger.

Obtained funding: Hershman, Unger.

Administrative, technical, or material support: Hershman.

Supervision: Hershman, Unger.

Conflict of Interest Disclosures: Dr LeBlanc reported grants from the National Institutes of Health outside the submitted work. No other disclosures were reported.

Funding/Support: This study was supported by grant UG1CA189974 from the National Institutes of Health National Cancer Institute.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

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