clinical research study definition

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What Are Clinical Trials and Studies?

On this page:

What is clinical research?

Why participate in a clinical trial, what happens in a clinical trial or study, what happens when a clinical trial or study ends, what are the different phases of clinical trials, questions to ask before participating in clinical research, how do researchers decide who will participate, clinical research needs participants with diverse backgrounds.

By participating in clinical research, you can help scientists develop new medications and other strategies to treat and prevent disease. Many effective treatments that are used today, such as chemotherapy, cholesterol-lowering drugs, vaccines, and cognitive-behavioral therapy, would not exist without research participants. Whether you’re healthy or have a medical condition, people of all ages and backgrounds can participate in clinical trials. This article can help you learn more about clinical research, why people choose to participate, and how to get involved in a study.

Mr. Jackson's story

Mr. Jackson is 73 years old and was just diagnosed with Alzheimer’s disease . He is worried about how it will affect his daily life. Will he forget to take his medicine? Will he forget his favorite memories, like the births of his children or hiking the Appalachian Trail? When Mr. Jackson talked to his doctor about his concerns, she told him about a clinical trial that is testing a possible new Alzheimer’s treatment. But Mr. Jackson has concerns about clinical trials. He does not want to feel like a lab rat or take the chance of getting a treatment that may not work or could make him feel worse. The doctor explained that there are both risks and benefits to being part of a clinical trial, and she talked with Mr. Jackson about research studies — what they are, how they work, and why they need volunteers. This information helped Mr. Jackson feel better about clinical trials. He plans to learn more about how to participate.

Clinical research is the study of health and illness in people. There are two main types of clinical research: observational studies and clinical trials.

Observational studies monitor people in normal settings. Researchers gather information from people and compare changes over time. For example, researchers may ask a group of older adults about their exercise habits and provide monthly memory tests for a year to learn how physical activity is associated with cognitive health . Observational studies do not test a medical intervention, such as a drug or device, but may help identify new treatments or prevention strategies to test in clinical trials.

Clinical trials are research studies that test a medical, surgical, or behavioral intervention in people. These trials are the primary way that researchers determine if a new form of treatment or prevention, such as a new drug, diet, or medical device (for example, a pacemaker), is safe and effective in people. Often, a clinical trial is designed to learn if a new treatment is more effective or has less harmful side effects than existing treatments.

Other aims of clinical research include:

• Testing ways to diagnose a disease early, sometimes before there are symptoms
• Finding approaches to prevent a health problem, including in people who are healthy but at increased risk of developing a disease
• Improving quality of life for people living with a life-threatening disease or chronic health problem
• Studying the role of caregivers or support groups

Learn more about clinical research from MedlinePlus and ClinicalTrials.gov .

People volunteer for clinical trials and studies for a variety of reasons, including:

• They want to contribute to discovering health information that may help others in the future.
• Participating in research helps them feel like they are playing a more active role in their health.
• The treatments they have tried for their health problem did not work or there is no treatment for their health problem.

Whatever the motivation, when you choose to participate in a clinical trial, you become a partner in scientific discovery. Participating in research can help future generations lead healthier lives. Major medical breakthroughs could not happen without the generosity of clinical trial participants — young and old, healthy, or diagnosed with a disease.

Where can I find a clinical trial?

Looking for clinical trials related to aging and age-related health conditions? Talk to your health care provider and use online resources to:

• Search for a clinical trial
• Look for clinical trials on Alzheimer's, other dementias, and caregiving
• Find a registry for a particular diagnosis or condition
• Explore clinical trials and studies supported by NIA

After you find one or more studies that you are interested in, the next step is for you or your doctor to contact the study research staff and ask questions. You can usually find contact information in the description of the study.

Let your health care provider know if you are thinking about joining a clinical trial. Your provider may want to talk to the research team to make sure the study is safe for you and to help coordinate your care.

Joining a clinical trial is a personal decision with potential benefits and some risks. Learn what happens in a clinical trial and how participant safety is protected . Read and listen to testimonials from people who decided to participate in research.

Here’s what typically happens in a clinical trial or study:

• Research staff explain the trial or study in detail, answer your questions, and gather more information about you.
• Once you agree to participate, you sign an informed consent form indicating your understanding about what to expect as a participant and the various outcomes that could occur.
• You are screened to make sure you qualify for the trial or study.
• If accepted into the trial, you schedule a first visit, which is called the “baseline” visit. The researchers conduct cognitive and/or physical tests during this visit.
• For some trials testing an intervention, you are assigned by chance (randomly) to a treatment group or a control group . The treatment group will get the intervention being tested, and the control group will not.
• You follow the trial procedures and report any issues or concerns to researchers.
• You may visit the research site at regularly scheduled times for new cognitive, physical, or other evaluations and discussions with staff. During these visits, the research team collects data and monitors your safety and well-being.
• You continue to see your regular physician(s) for usual health care throughout the study.

How do researchers decide which interventions are safe to test in people?

Before a clinical trial is designed and launched, scientists perform laboratory tests and often conduct studies in animals to test a potential intervention’s safety and effectiveness. If these studies show favorable results, the U.S. Food and Drug Administration (FDA) approves the intervention to be tested in humans. Learn more about how the safety of clinical trial participants is protected.

Once a clinical trial or study ends, the researchers analyze the data to determine what the findings mean and to plan the next steps. As a participant, you should be provided information before the study starts about how long it will last, whether you will continue receiving the treatment after the trial ends (if applicable), and how the results of the research will be shared. If you have specific questions about what will happen when the trial or study ends, ask the research coordinator or staff.

Clinical trials of drugs and medical devices advance through several phases to test safety, determine effectiveness, and identify any side effects. The FDA typically requires Phase 1, 2, and 3 trials to be conducted to determine if the drug or device can be approved for further use. If researchers find the intervention to be safe and effective after the first three phases, the FDA approves it for clinical use and continues to monitor its effects.

Each phase has a different purpose:

• A Phase 1 trial tests an experimental drug or device on a small group of people (around 20 to 80) to judge its safety, including any side effects, and to test the amount (dosage).
• A Phase 2 trial includes more people (around 100 to 300) to help determine whether a drug is effective. This phase aims to obtain preliminary data on whether the drug or device works in people who have a certain disease or condition. These trials also continue to examine safety, including short-term side effects.
• A Phase 3 trial gathers additional information from several hundred to a few thousand people about safety and effectiveness, studying different populations and different dosages, and comparing the intervention with other drugs or treatment approaches. If the FDA agrees that the trial results support the intervention’s use for a particular health condition, it will approve the experimental drug or device.
• A Phase 4 trial takes place after the FDA approves the drug or device. The treatment’s effectiveness and safety are monitored in large, diverse populations. Sometimes, side effects may not become clear until more people have used the drug or device over a longer period of time.

Clinical trials that test a behavior change, rather than a drug or medical device, advance through similar steps, but behavioral interventions are not regulated by the FDA. Learn more about clinical trials , including the types of trials and the four phases.

Choosing to participate in research is an important personal decision. If you are considering joining a trial or study, get answers to your questions and know your options before you decide. Here are questions you might ask the research team when thinking about participating.

• What is this study trying to find out?
• What treatment or tests will I have? Will they hurt? Will you provide me with the test or lab results?
• What are the chances I will be in the experimental group or the control group?
• If the study tests a treatment, what are the possible risks, side effects, and benefits compared with my current treatment?
• How long will the clinical trial last?
• Where will the study take place? Will I need to stay in the hospital?
• Will you provide a way for me to get to the study site if I need it, such as through a ride-share service?
• Will I need a trial or study partner (for example, a family member or friend who knows me well) to come with me to the research site visits? If so, how long will he or she need to participate?
• Can I participate in any part of the trial with my regular doctor or at a clinic closer to my home?
• How will the study affect my everyday life?
• What steps are being taken to ensure my privacy?
• How will you protect my health while I participate?
• What happens if my health problem gets worse during the trial or study?
• Can I take my regular medicines while participating?
• Who will be in charge of my care while I am in the trial or study? Will I be able to see my own doctors?
• How will you keep my doctor informed about my participation?
• If I withdraw from the trial or study, will this affect my normal care?
• Will it cost me anything to be in the trial or study? If so, will I be reimbursed for expenses, such as travel, parking, lodging, or meals?
• Will my insurance pay for costs not covered by the research, or must I pay out of pocket? If I don’t have insurance, am I still eligible to participate?
• Will my trial or study partner be compensated for his or her time?
• Will you follow up on my health after the end of the trial or study?
• Will I continue receiving the treatment after the trial or study ends?
• Will you tell me the results of the research?
• Whom do I contact if I have questions after the trial or study ends?

To be eligible to participate, you may need to have certain characteristics, called inclusion criteria. For example, a clinical trial may need participants to have a certain stage of disease, version of a gene, or family history. Some trials require that participants have a study partner who can accompany them to clinic visits.

Participants with certain characteristics may not be allowed to participate in some trials. These characteristics are called exclusion criteria. They include factors such as specific health conditions or medications that could interfere with the treatment being tested.

Many volunteers must be screened to find enough people who are eligible for a trial or study. Generally, you can participate in only one clinical trial at a time, although this is not necessarily the case for observational studies. Different trials have different criteria, so being excluded from one trial does not necessarily mean you will be excluded from another.

When research only includes people with similar backgrounds, the findings may not apply to or benefit a broader population. The results of clinical trials and studies with diverse participants may apply to more people. That’s why research benefits from having participants of different ages, sexes, races, and ethnicities.

Researchers need older adults to participate in clinical research so that scientists can learn more about how new drugs, tests, and other interventions will work for them. Many older adults have health needs that are different from those of younger people. For example, as people age, their bodies may react differently to certain drugs. Older adults may need different dosages of a drug to have the intended result. Also, some drugs may have different side effects in older people than in younger individuals. Having older adults enrolled in clinical trials and studies helps researchers get the information they need to develop the right treatments for this age group.

Researchers know that it may be challenging for some older adults to join a clinical trial or study. For example, if you have multiple health problems, can you participate in research that is looking at only one condition? If you are frail or have a disability, will you be strong enough to participate? If you no longer drive, how can you get to the research site? Talk to the research coordinator or staff about your concerns. The research team may have already thought about some of the potential obstacles and have a plan to make it easier for you to participate.

Read more about diversity in clinical trials .

You may also be interested in

• Learning more about the benefits, risks, and safety of clinical research
• Finding out about participating in Alzheimer's disease research
• Downloading or sharing an infographic with the benefits of participating in clinical research

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For more information about clinical trials.

Alzheimers.gov www.alzheimers.gov Explore the Alzheimers.gov website for information and resources on Alzheimer’s and related dementias from across the federal government.

Clinical Research Trials and You National Institutes of Health www.nih.gov/health-information/nih-clinical-research-trials-you

ClinicalTrials.gov www.clinicaltrials.gov 

This content is provided by the NIH National Institute on Aging (NIA). NIA scientists and other experts review this content to ensure it is accurate and up to date.

Content reviewed: March 22, 2023

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NIH's Definition of a Clinical Trial

This page provides information, tools, and resources about the definition of a clinical trial. Correctly identifying whether a study is considered by NIH to be a clinical trial is crucial to how you will:

• Select the right NIH funding opportunity for your research study
• Write the research strategy and human subjects sections of your grant application and contract proposal
• Comply with appropriate policies and regulations, including registration and reporting in ClinicalTrials.gov

Note: Misclassified clinical trial applications may be withdrawn.

In 2016, NIH launched a multi-faceted effort to enhance its stewardship over clinical trials. The goal of this effort is to encourage advances in the design, conduct, and oversight of clinical trials while elevating the entire biomedical research enterprise to a new level of transparency and accountability. The NIH definition of a clinical trial was revised in 2014 in anticipation of these stewardship reforms to ensure a clear and responsive definition of a clinical trial. Learn more about why NIH has made changes to improve clinical trial stewardship.

NIH Definition of a Clinical Trial

A research study in which one or more human subjects are prospectively assigned prospectively assigned The term "prospectively assigned" refers to a pre-defined process (e.g., randomization) specified in an approved protocol that stipulates the assignment of research subjects (individually or in clusters) to one or more arms (e.g., intervention, placebo, or other control) of a clinical trial. to one or more interventions interventions An "intervention" is defined as a manipulation of the subject or subject’s environment for the purpose of modifying one or more health-related biomedical or behavioral processes and/or endpoints. Examples include: drugs/small molecules/compounds; biologics; devices; procedures (e.g., surgical techniques); delivery systems (e.g., telemedicine, face-to-face interviews); strategies to change health-related behavior (e.g., diet, cognitive therapy, exercise, development of new habits); treatment strategies; prevention strategies; and, diagnostic strategies. (which may include placebo or other control) to evaluate the effects of those interventions on health-related biomedical or behavioral outcomes. health-related biomedical or behavioral outcomes. A "health-related biomedical or behavioral outcome" is defined as the pre-specified goal(s) or condition(s) that reflect the effect of one or more interventions on human subjects’ biomedical or behavioral status or quality of life. Examples include: positive or negative changes to physiological or biological parameters (e.g., improvement of lung capacity, gene expression); positive or negative changes to psychological or neurodevelopmental parameters (e.g., mood management intervention for smokers; reading comprehension and /or information retention); positive or negative changes to disease processes; positive or negative changes to health-related behaviors; and, positive or negative changes to quality of life.   

DECISION TOOL

Your human subjects study may meet the NIH definition of a clinical trial.

FIND OUT HERE

Use the following four questions to determine the difference between a clinical study and a clinical trial:

• Does the study involve human participants?
• Are the participants prospectively assigned to an intervention?
• Is the study designed to evaluate the effect of the intervention on the participants?
• Is the effect being evaluated a health-related biomedical or behavioral outcome?

Note that if the answers to the 4 questions are yes, your study meets the NIH definition of a clinical trial, even if…

• You are studying healthy participants
• Your study does not have a comparison group (e.g., placebo or control), or has a single arm
• Your study is only designed to assess the pharmacokinetics, safety, and/or maximum tolerated dose of an investigational drug
• Your study is utilizing a behavioral intervention, or measuring intent to change behavior
• Only one aim or sub-aim of your study meets the clinical trial definition
• Your study is no more than minimal risk

Studies intended solely to refine measures are not considered clinical trials. Studies that solely involve secondary research with biological specimens or health information are not clinical trials.

Resources to Clarify the Definition

Case studies.

These simplified case studies illustrate the differences between clinical trials and clinical studies.

Decision Tree

Print this decision tree for an easy reference for the four questions that identify a clinical trial.

Related Guide Notice

NOT-OD-15-015   Notice of Revised NIH Definition of “Clinical Trial”

This page last updated on: June 10, 2024

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About Clinical Trials

What is a clinical trial.

Clinical trials look at new ways to prevent, detect, or treat disease. The goal of clinical trials is to determine if a new test or treatment works and is safe. 

The idea for a clinical trial —also known as a clinical research study —often originates in the laboratory. After researchers test new therapies or procedures in the laboratory and in animal studies, the most promising experimental treatments are moved into clinical trials, which are conducted in phases. During a trial, more information is gained about an experimental treatment, its risks, and its effectiveness.

Types of Clinical Trials

• Natural history studies provide valuable information about how disease and health progress.
• Prevention trials look for better ways to prevent a disease in people who have never had the disease or to prevent the disease from returning. Better approaches may include medicines, vaccines, or lifestyle changes, among other things.
• Screening trials test the best way to detect certain diseases or health conditions.
• Diagnostic trials determine better tests or procedures for diagnosing a particular disease or condition.
• Treatment trials test new treatments, new combinations of drugs, or new approaches to surgery or radiation therapy.
• Quality of life trials (or supportive care trials) explore and measure ways to improve the comfort and quality of life of people with a chronic illness.

Clinical Trial Phases

Clinical trials are conducted in phases. Each phase has a different purpose and helps researchers answer different questions.

• Phase I trials: Researchers test an experimental drug or treatment in a small group of people (20–80) for the first time. The purpose is to evaluate its safety and identify side effects.
• Phase II trials: The experimental drug or treatment is administered to a larger group of people (100–300) to determine its effectiveness and to further evaluate its safety.
• Phase III trials: The experimental drug or treatment is administered to large groups of people (1,000–3,000) to confirm its effectiveness, monitor side effects, compare it with standard or equivalent treatments, and collect information that will allow the experimental drug or treatment to be used safely.
• Phase IV trials: After a drug is approved by the FDA and made available to the public, researchers track its safety, seeking more information about a drug or treatment’s risks, benefits, and optimal use.

For more information about clinical trials, see the webpage at National Institute of Health.

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What is a Clinical Trial?

Clinical trials are medical research studies in which people participate as volunteers. They help researchers better understand the normal biological processes, learn more about diseases and conditions, and develop new treatments and medications.

Find a Clinical Trial

There are several ways to search for clinical trials that may be right for you.

NIAMS Clinical Studies See the full list of featured NIAMS clinical trials taking place at the NIH Clinical Center.

NIH Clinical Studies Find clinical trials sponsored by one or more of NIH’s Institutes and Centers.

ClinicalTrials.gov Search a database of clinical trials available across the country and around the globe. To search for studies accepting healthy volunteers, type in the keywords: 'healthy' and 'normal'.

Resource Information (ClinicalTrials.gov) Find publications related to ClinicalTrials.gov, clinical alerts, and information on subscribing to RSS feeds for new and updated clinical studies.

Participating in a Trial

Once you’ve decided to participate in a trial, learn more about what happens find information to help Get answers to your questions about participating in a clinical trial.

Patient Information Get answers to all of your questions, including the admission process, the NIH campus, and your protections as a volunteer.

Healthy Volunteer Information Find out why healthy volunteers are needed and how you can volunteer to participate in a clinical study.

Glossary of Clinical Trials Terms Learn what all the terms mean so you can better understand clinical trials and make an informed decision about whether to participate.

Clinical Trial Basics

Learn about clinical trials, who conducts them, where they are conducted, why you might participate, and the benefits and risks to participating.

NIH Clinical Research Trials and You Learn what clinical trials are, who participates, questions to ask about the trials, and your protections as a volunteer.

Are Clinical Studies for You? Learn about the risks and benefits of participating in a clinical trial and the questions to discuss with your doctor as you think about whether participating is right for you.

Learn About Clinical Studies Learn the basics about clinical studies, why they are important, and who can participate in a study.

For Health Providers

Referring Physician Information Find strategies and tips for how to refer patients to clinical trials and how to stay involved once your patient is in a trial.

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Clinical trials

Clinical trials are a type of research that studies new tests and treatments and evaluates their effects on human health outcomes. People volunteer to take part in clinical trials to test medical interventions including drugs, cells and other biological products, surgical procedures, radiological procedures, devices, behavioural treatments and preventive care.

Clinical trials are carefully designed, reviewed and completed, and need to be approved before they can start. People of all ages can take part in clinical trials, including children.

There are 4 phases of biomedical clinical trials:

• Phase I studies usually test new drugs for the first time in a small group of people to evaluate a safe dosage range and identify side effects.
• Phase II studies test treatments that have been found to be safe in phase I but now need a larger group of human subjects to monitor for any adverse effects.
• Phase III studies are conducted on larger populations and in different regions and countries, and are often the step right before a new treatment is approved.
• Phase IV studies take place after country approval and there is a need for further testing in a wide population over a longer timeframe.

Clinical trials usually involve participants from more than one medical or research institution, and often more than one country. As each country has its own requirements for clinical trials research it is possible that single trials could be included on more than one registry, and hence appear on more than one registry database. However, data on various clinical trial registries varies.

WHO’s International Clinical Trials Registry Platform (ICTRP) links clinical trials registers globally in order to ensure a single point of access and the unambiguous identification of trials with a view to enhancing access to information by patients, families, patient groups and others.

The ICTRP is a global initiative that aims to make information about all clinical trials involving humans publicly available. It also aims to:

• improve the comprehensiveness, completeness and accuracy of registered clinical trial data;
• communicate and raise awareness of the need to register clinical trials;
• ensure the accessibility of registered data;
• build capacity for clinical trial registration;
• encourage the utilization of registered data; and
• ensure the sustainability of the ICTRP.
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What is Clinical Research?

Clinical research is the study of health and illness in people. It is the way we learn how to prevent, diagnose and treat illness. Clinical research describes many different elements of scientific investigation. Simply put, it involves human participants and helps translate basic research (done in labs) into new treatments and information to benefit patients. Clinical trials as well as research in epidemiology, physiology and pathophysiology, health services, education, outcomes and mental health can all fall under the clinical research umbrella.

Clinical Trials

A clinical trial is a type of clinical research study. A clinical trial is an experiment designed to answer specific questions about possible new treatments or new ways of using existing (known) treatments. Clinical trials are done to determine whether new drugs or treatments are safe and effective. Clinical trials are part of a long, careful process which may take many years to complete. First, doctors study a new treatment in the lab. Then they often study the treatment in animals. If a new treatment shows promise, doctors then test the treatment in people via a clinical trial.

Clinical Research vs. Medical Care

People often confuse a clinical research or clinical trials with medical care. This topic can be especially confusing if your doctor is also the researcher.  When you receive medical care from your own doctor, he or she develops a plan of care just for you. When you take part in a clinical research study, you and the researcher must follow a set plan called the “study protocol.” The researcher usually can’t adjust the plan for you – but the plan includes steps to follow if you aren’t doing well. It’s important to understand that a clinical trial is an experiment. By its nature, that means the answer to the research question is still unknown. You might or might not benefit directly by participating in a clinical research study. It is important to talk about this topic with your doctor/the researcher.

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Step 3: Clinical Research

While preclinical research answers basic questions about a drug’s safety, it is not a substitute for studies of ways the drug will interact with the human body. “Clinical research” refers to studies, or trials, that are done in people. As the developers design the clinical study, they will consider what they want to accomplish for each of the different Clinical Research Phases and begin the Investigational New Drug Process (IND), a process they must go through before clinical research begins.

On this page you will find information on:

Designing Clinical Trials

Clinical Research Phase Studies

The Investigational New Drug Process

Asking for FDA Assistance

FDA IND Review Team

Researchers design clinical trials to answer specific research questions related to a medical product. These trials follow a specific study plan, called a protocol , that is developed by the researcher or manufacturer. Before a clinical trial begins, researchers review prior information about the drug to develop research questions and objectives. Then, they decide:

Who qualifies to participate (selection criteria)

How many people will be part of the study

How long the study will last

Whether there will be a control group and other ways to limit research bias

How the drug will be given to patients and at what dosage

What assessments will be conducted, when, and what data will be collected

How the data will be reviewed and analyzed

Clinical trials follow a typical series from early, small-scale, Phase 1 studies to late-stage, large scale, Phase 3 studies.

What are the Clinical Trial Phases?

Watch this video to learn about the three phases of clinical trials.

Study Participants: 20 to 100 healthy volunteers or people with the disease/condition.

Length of Study: Several months

Purpose: Safety and dosage

During Phase 1 studies, researchers test a new drug in normal volunteers (healthy people). In most cases, 20 to 80 healthy volunteers or people with the disease/condition participate in Phase 1. However, if a new drug is intended for use in cancer patients, researchers conduct Phase 1 studies in patients with that type of cancer.

Phase 1 studies are closely monitored and gather information about how a drug interacts with the human body. Researchers adjust dosing schemes based on animal data to find out how much of a drug the body can tolerate and what its acute side effects are.

As a Phase 1 trial continues, researchers answer research questions related to how it works in the body, the side effects associated with increased dosage, and early information about how effective it is to determine how best to administer the drug to limit risks and maximize possible benefits. This is important to the design of Phase 2 studies.

Approximately 70% of drugs move to the next phase

Study Participants: Up to several hundred people with the disease/condition.

Length of Study: Several months to 2 years

Purpose: Efficacy and side effects

In Phase 2 studies, researchers administer the drug to a group of patients with the disease or condition for which the drug is being developed. Typically involving a few hundred patients, these studies aren't large enough to show whether the drug will be beneficial.

Instead, Phase 2 studies provide researchers with additional safety data. Researchers use these data to refine research questions, develop research methods, and design new Phase 3 research protocols.

Approximately 33% of drugs move to the next phase

Study Participants: 300 to 3,000 volunteers who have the disease or condition

Length of Study: 1 to 4 years

Purpose: Efficacy and monitoring of adverse reactions

Researchers design Phase 3 studies to demonstrate whether or not a product offers a treatment benefit to a specific population. Sometimes known as pivotal studies, these studies involve 300 to 3,000 participants.

Phase 3 studies provide most of the safety data. In previous studies, it is possible that less common side effects might have gone undetected. Because these studies are larger and longer in duration, the results are more likely to show long-term or rare side effects

Approximately 25-30% of drugs move to the next phase

Study Participants: Several thousand volunteers who have the disease/condition

Purpose: Safety and efficacy

Phase 4 trials are carried out once the drug or device has been approved by FDA during the Post-Market Safety Monitoring

Learn more about Clinical Trials .

Drug developers, or sponsors , must submit an Investigational New Drug (IND) application to FDA before beginning clinical research.

In the IND application, developers must include:

Animal study data and toxicity (side effects that cause great harm) data

Manufacturing information

Clinical protocols (study plans) for studies to be conducted

Data from any prior human research

Information about the investigator

Drug developers are free to ask for help from FDA at any point in the drug development process, including:

Pre-IND application, to review FDA guidance documents and get answers to questions that may help enhance their research

After Phase 2, to obtain guidance on the design of large Phase 3 studies

Any time during the process, to obtain an assessment of the IND application

Even though FDA offers extensive technical assistance, drug developers are not required to take FDA’s suggestions. As long as clinical trials are thoughtfully designed, reflect what developers know about a product, safeguard participants, and otherwise meet Federal standards, FDA allows wide latitude in clinical trial design.

The review team consists of a group of specialists in different scientific fields. Each member has different responsibilities.

Project Manager: Coordinates the team’s activities throughout the review process, and is the primary contact for the sponsor.

Medical Officer: Reviews all clinical study information and data before, during, and after the trial is complete.

Statistician: Interprets clinical trial designs and data, and works closely with the medical officer to evaluate protocols and safety and efficacy data.

Pharmacologist: Reviews preclinical studies.

Pharmakineticist: Focuses on the drug’s absorption, distribution, metabolism, and excretion processes.Interprets blood-level data at different time intervals from clinical trials, as a way to assess drug dosages and administration schedules.

Chemist: Evaluates a drug’s chemical compounds. Analyzes how a drug was made and its stability, quality control, continuity, the presence of impurities, etc.

Microbiologist: Reviews the data submitted, if the product is an antimicrobial product, to assess response across different classes of microbes.

The FDA review team has 30 days to review the original IND submission. The process protects volunteers who participate in clinical trials from unreasonable and significant risk in clinical trials. FDA responds to IND applications in one of two ways:

Approval to begin clinical trials.

Clinical hold to delay or stop the investigation. FDA can place a clinical hold for specific reasons, including:

Participants are exposed to unreasonable or significant risk.

Investigators are not qualified.

Materials for the volunteer participants are misleading.

The IND application does not include enough information about the trial’s risks.

A clinical hold is rare; instead, FDA often provides comments intended to improve the quality of a clinical trial. In most cases, if FDA is satisfied that the trial meets Federal standards, the applicant is allowed to proceed with the proposed study.

The developer is responsible for informing the review team about new protocols, as well as serious side effects seen during the trial. This information ensures that the team can monitor the trials carefully for signs of any problems. After the trial ends, researchers must submit study reports.

This process continues until the developer decides to end clinical trials or files a marketing application. Before filing a marketing application, a developer must have adequate data from two large, controlled clinical trials.

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• v.11(2); 2019 Feb

Planning and Conducting Clinical Research: The Whole Process

Boon-how chew.

1 Family Medicine, Universiti Putra Malaysia, Serdang, MYS

The goal of this review was to present the essential steps in the entire process of clinical research. Research should begin with an educated idea arising from a clinical practice issue. A research topic rooted in a clinical problem provides the motivation for the completion of the research and relevancy for affecting medical practice changes and improvements. The research idea is further informed through a systematic literature review, clarified into a conceptual framework, and defined into an answerable research question. Engagement with clinical experts, experienced researchers, relevant stakeholders of the research topic, and even patients can enhance the research question’s relevance, feasibility, and efficiency. Clinical research can be completed in two major steps: study designing and study reporting. Three study designs should be planned in sequence and iterated until properly refined: theoretical design, data collection design, and statistical analysis design. The design of data collection could be further categorized into three facets: experimental or non-experimental, sampling or census, and time features of the variables to be studied. The ultimate aims of research reporting are to present findings succinctly and timely. Concise, explicit, and complete reporting are the guiding principles in clinical studies reporting.

Introduction and background

Medical and clinical research can be classified in many different ways. Probably, most people are familiar with basic (laboratory) research, clinical research, healthcare (services) research, health systems (policy) research, and educational research. Clinical research in this review refers to scientific research related to clinical practices. There are many ways a clinical research's findings can become invalid or less impactful including ignorance of previous similar studies, a paucity of similar studies, poor study design and implementation, low test agent efficacy, no predetermined statistical analysis, insufficient reporting, bias, and conflicts of interest [ 1 - 4 ]. Scientific, ethical, and moral decadence among researchers can be due to incognizant criteria in academic promotion and remuneration and too many forced studies by amateurs and students for the sake of research without adequate training or guidance [ 2 , 5 - 6 ]. This article will review the proper methods to conduct medical research from the planning stage to submission for publication (Table ​ (Table1 1 ).

a Feasibility and efficiency are considered during the refinement of the research question and adhered to during data collection.

Epidemiologic studies in clinical and medical fields focus on the effect of a determinant on an outcome [ 7 ]. Measurement errors that happen systematically give rise to biases leading to invalid study results, whereas random measurement errors will cause imprecise reporting of effects. Precision can usually be increased with an increased sample size provided biases are avoided or trivialized. Otherwise, the increased precision will aggravate the biases. Because epidemiologic, clinical research focuses on measurement, measurement errors are addressed throughout the research process. Obtaining the most accurate estimate of a treatment effect constitutes the whole business of epidemiologic research in clinical practice. This is greatly facilitated by clinical expertise and current scientific knowledge of the research topic. Current scientific knowledge is acquired through literature reviews or in collaboration with an expert clinician. Collaboration and consultation with an expert clinician should also include input from the target population to confirm the relevance of the research question. The novelty of a research topic is less important than the clinical applicability of the topic. Researchers need to acquire appropriate writing and reporting skills from the beginning of their careers, and these skills should improve with persistent use and regular reviewing of published journal articles. A published clinical research study stands on solid scientific ground to inform clinical practice given the article has passed through proper peer-reviews, revision, and content improvement.

Systematic literature reviews

Systematic literature reviews of published papers will inform authors of the existing clinical evidence on a research topic. This is an important step to reduce wasted efforts and evaluate the planned study [ 8 ]. Conducting a systematic literature review is a well-known important step before embarking on a new study [ 9 ]. A rigorously performed and cautiously interpreted systematic review that includes in-process trials can inform researchers of several factors [ 10 ]. Reviewing the literature will inform the choice of recruitment methods, outcome measures, questionnaires, intervention details, and statistical strategies – useful information to increase the study’s relevance, value, and power. A good review of previous studies will also provide evidence of the effects of an intervention that may or may not be worthwhile; this would suggest either no further studies are warranted or that further study of the intervention is needed. A review can also inform whether a larger and better study is preferable to an additional small study. Reviews of previously published work may yield few studies or low-quality evidence from small or poorly designed studies on certain intervention or observation; this may encourage or discourage further research or prompt consideration of a first clinical trial.

Conceptual framework

The result of a literature review should include identifying a working conceptual framework to clarify the nature of the research problem, questions, and designs, and even guide the latter discussion of the findings and development of possible solutions. Conceptual frameworks represent ways of thinking about a problem or how complex things work the way they do [ 11 ]. Different frameworks will emphasize different variables and outcomes, and their inter-relatedness. Each framework highlights or emphasizes different aspects of a problem or research question. Often, any single conceptual framework presents only a partial view of reality [ 11 ]. Furthermore, each framework magnifies certain elements of the problem. Therefore, a thorough literature search is warranted for authors to avoid repeating the same research endeavors or mistakes. It may also help them find relevant conceptual frameworks including those that are outside one’s specialty or system. 

Conceptual frameworks can come from theories with well-organized principles and propositions that have been confirmed by observations or experiments. Conceptual frameworks can also come from models derived from theories, observations or sets of concepts or even evidence-based best practices derived from past studies [ 11 ].

Researchers convey their assumptions of the associations of the variables explicitly in the conceptual framework to connect the research to the literature. After selecting a single conceptual framework or a combination of a few frameworks, a clinical study can be completed in two fundamental steps: study design and study report. Three study designs should be planned in sequence and iterated until satisfaction: the theoretical design, data collection design, and statistical analysis design [ 7 ]. 

Study designs

Theoretical Design

Theoretical design is the next important step in the research process after a literature review and conceptual framework identification. While the theoretical design is a crucial step in research planning, it is often dealt with lightly because of the more alluring second step (data collection design). In the theoretical design phase, a research question is designed to address a clinical problem, which involves an informed understanding based on the literature review and effective collaboration with the right experts and clinicians. A well-developed research question will have an initial hypothesis of the possible relationship between the explanatory variable/exposure and the outcome. This will inform the nature of the study design, be it qualitative or quantitative, primary or secondary, and non-causal or causal (Figure ​ (Figure1 1 ).

A study is qualitative if the research question aims to explore, understand, describe, discover or generate reasons underlying certain phenomena. Qualitative studies usually focus on a process to determine how and why things happen [ 12 ]. Quantitative studies use deductive reasoning, and numerical statistical quantification of the association between groups on data often gathered during experiments [ 13 ]. A primary clinical study is an original study gathering a new set of patient-level data. Secondary research draws on the existing available data and pooling them into a larger database to generate a wider perspective or a more powerful conclusion. Non-causal or descriptive research aims to identify the determinants or associated factors for the outcome or health condition, without regard for causal relationships. Causal research is an exploration of the determinants of an outcome while mitigating confounding variables. Table ​ Table2 2 shows examples of non-causal (e.g., diagnostic and prognostic) and causal (e.g., intervention and etiologic) clinical studies. Concordance between the research question, its aim, and the choice of theoretical design will provide a strong foundation and the right direction for the research process and path. 

A problem in clinical epidemiology is phrased in a mathematical relationship below, where the outcome is a function of the determinant (D) conditional on the extraneous determinants (ED) or more commonly known as the confounding factors [ 7 ]:

For non-causal research, Outcome = f (D1, D2…Dn) For causal research, Outcome = f (D | ED)

A fine research question is composed of at least three components: 1) an outcome or a health condition, 2) determinant/s or associated factors to the outcome, and 3) the domain. The outcome and the determinants have to be clearly conceptualized and operationalized as measurable variables (Table ​ (Table3; 3 ; PICOT [ 14 ] and FINER [ 15 ]). The study domain is the theoretical source population from which the study population will be sampled, similar to the wording on a drug package insert that reads, “use this medication (study results) in people with this disease” [ 7 ].

The interpretation of study results as they apply to wider populations is known as generalization, and generalization can either be statistical or made using scientific inferences [ 16 ]. Generalization supported by statistical inferences is seen in studies on disease prevalence where the sample population is representative of the source population. By contrast, generalizations made using scientific inferences are not bound by the representativeness of the sample in the study; rather, the generalization should be plausible from the underlying scientific mechanisms as long as the study design is valid and nonbiased. Scientific inferences and generalizations are usually the aims of causal studies. 

Confounding: Confounding is a situation where true effects are obscured or confused [ 7 , 16 ]. Confounding variables or confounders affect the validity of a study’s outcomes and should be prevented or mitigated in the planning stages and further managed in the analytical stages. Confounders are also known as extraneous determinants in epidemiology due to their inherent and simultaneous relationships to both the determinant and outcome (Figure ​ (Figure2), 2 ), which are usually one-determinant-to-one outcome in causal clinical studies. The known confounders are also called observed confounders. These can be minimized using randomization, restriction, or a matching strategy. Residual confounding has occurred in a causal relationship when identified confounders were not measured accurately. Unobserved confounding occurs when the confounding effect is present as a variable or factor not observed or yet defined and, thus, not measured in the study. Age and gender are almost universal confounders followed by ethnicity and socio-economic status.

Confounders have three main characteristics. They are a potential risk factor for the disease, associated with the determinant of interest, and should not be an intermediate variable between the determinant and the outcome or a precursor to the determinant. For example, a sedentary lifestyle is a cause for acute coronary syndrome (ACS), and smoking could be a confounder but not cardiorespiratory unfitness (which is an intermediate factor between a sedentary lifestyle and ACS). For patients with ACS, not having a pair of sports shoes is not a confounder – it is a correlate for the sedentary lifestyle. Similarly, depression would be a precursor, not a confounder.

Sample size consideration: Sample size calculation provides the required number of participants to be recruited in a new study to detect true differences in the target population if they exist. Sample size calculation is based on three facets: an estimated difference in group sizes, the probability of α (Type I) and β (Type II) errors chosen based on the nature of the treatment or intervention, and the estimated variability (interval data) or proportion of the outcome (nominal data) [ 17 - 18 ]. The clinically important effect sizes are determined based on expert consensus or patients’ perception of benefit. Value and economic consideration have increasingly been included in sample size estimations. Sample size and the degree to which the sample represents the target population affect the accuracy and generalization of a study’s reported effects. 

Pilot study: Pilot studies assess the feasibility of the proposed research procedures on small sample size. Pilot studies test the efficiency of participant recruitment with minimal practice or service interruptions. Pilot studies should not be conducted to obtain a projected effect size for a larger study population because, in a typical pilot study, the sample size is small, leading to a large standard error of that effect size. This leads to bias when projected for a large population. In the case of underestimation, this could lead to inappropriately terminating the full-scale study. As the small pilot study is equally prone to bias of overestimation of the effect size, this would lead to an underpowered study and a failed full-scale study [ 19 ]. 

The Design of Data Collection

The “perfect” study design in the theoretical phase now faces the practical and realistic challenges of feasibility. This is the step where different methods for data collection are considered, with one selected as the most appropriate based on the theoretical design along with feasibility and efficiency. The goal of this stage is to achieve the highest possible validity with the lowest risk of biases given available resources and existing constraints. 

In causal research, data on the outcome and determinants are collected with utmost accuracy via a strict protocol to maximize validity and precision. The validity of an instrument is defined as the degree of fidelity of the instrument, measuring what it is intended to measure, that is, the results of the measurement correlate with the true state of an occurrence. Another widely used word for validity is accuracy. Internal validity refers to the degree of accuracy of a study’s results to its own study sample. Internal validity is influenced by the study designs, whereas the external validity refers to the applicability of a study’s result in other populations. External validity is also known as generalizability and expresses the validity of assuming the similarity and comparability between the study population and the other populations. Reliability of an instrument denotes the extent of agreeableness of the results of repeated measurements of an occurrence by that instrument at a different time, by different investigators or in a different setting. Other terms that are used for reliability include reproducibility and precision. Preventing confounders by identifying and including them in data collection will allow statistical adjustment in the later analyses. In descriptive research, outcomes must be confirmed with a referent standard, and the determinants should be as valid as those found in real clinical practice.

Common designs for data collection include cross-sectional, case-control, cohort, and randomized controlled trials (RCTs). Many other modern epidemiology study designs are based on these classical study designs such as nested case-control, case-crossover, case-control without control, and stepwise wedge clustered RCTs. A cross-sectional study is typically a snapshot of the study population, and an RCT is almost always a prospective study. Case-control and cohort studies can be retrospective or prospective in data collection. The nested case-control design differs from the traditional case-control design in that it is “nested” in a well-defined cohort from which information on the cohorts can be obtained. This design also satisfies the assumption that cases and controls represent random samples of the same study base. Table ​ Table4 4 provides examples of these data collection designs.

Additional aspects in data collection: No single design of data collection for any research question as stated in the theoretical design will be perfect in actual conduct. This is because of myriad issues facing the investigators such as the dynamic clinical practices, constraints of time and budget, the urgency for an answer to the research question, and the ethical integrity of the proposed experiment. Therefore, feasibility and efficiency without sacrificing validity and precision are important considerations in data collection design. Therefore, data collection design requires additional consideration in the following three aspects: experimental/non-experimental, sampling, and timing [ 7 ]:

Experimental or non-experimental: Non-experimental research (i.e., “observational”), in contrast to experimental, involves data collection of the study participants in their natural or real-world environments. Non-experimental researches are usually the diagnostic and prognostic studies with cross-sectional in data collection. The pinnacle of non-experimental research is the comparative effectiveness study, which is grouped with other non-experimental study designs such as cross-sectional, case-control, and cohort studies [ 20 ]. It is also known as the benchmarking-controlled trials because of the element of peer comparison (using comparable groups) in interpreting the outcome effects [ 20 ]. Experimental study designs are characterized by an intervention on a selected group of the study population in a controlled environment, and often in the presence of a similar group of the study population to act as a comparison group who receive no intervention (i.e., the control group). Thus, the widely known RCT is classified as an experimental design in data collection. An experimental study design without randomization is referred to as a quasi-experimental study. Experimental studies try to determine the efficacy of a new intervention on a specified population. Table ​ Table5 5 presents the advantages and disadvantages of experimental and non-experimental studies [ 21 ].

a May be an issue in cross-sectional studies that require a long recall to the past such as dietary patterns, antenatal events, and life experiences during childhood.

Once an intervention yields a proven effect in an experimental study, non-experimental and quasi-experimental studies can be used to determine the intervention’s effect in a wider population and within real-world settings and clinical practices. Pragmatic or comparative effectiveness are the usual designs used for data collection in these situations [ 22 ].

Sampling/census: Census is a data collection on the whole source population (i.e., the study population is the source population). This is possible when the defined population is restricted to a given geographical area. A cohort study uses the census method in data collection. An ecologic study is a cohort study that collects summary measures of the study population instead of individual patient data. However, many studies sample from the source population and infer the results of the study to the source population for feasibility and efficiency because adequate sampling provides similar results to the census of the whole population. Important aspects of sampling in research planning are sample size and representation of the population. Sample size calculation accounts for the number of participants needed to be in the study to discover the actual association between the determinant and outcome. Sample size calculation relies on the primary objective or outcome of interest and is informed by the estimated possible differences or effect size from previous similar studies. Therefore, the sample size is a scientific estimation for the design of the planned study.

A sampling of participants or cases in a study can represent the study population and the larger population of patients in that disease space, but only in prevalence, diagnostic, and prognostic studies. Etiologic and interventional studies do not share this same level of representation. A cross-sectional study design is common for determining disease prevalence in the population. Cross-sectional studies can also determine the referent ranges of variables in the population and measure change over time (e.g., repeated cross-sectional studies). Besides being cost- and time-efficient, cross-sectional studies have no loss to follow-up; recall bias; learning effect on the participant; or variability over time in equipment, measurement, and technician. A cross-sectional design for an etiologic study is possible when the determinants do not change with time (e.g., gender, ethnicity, genetic traits, and blood groups). 

In etiologic research, comparability between the exposed and the non-exposed groups is more important than sample representation. Comparability between these two groups will provide an accurate estimate of the effect of the exposure (risk factor) on the outcome (disease) and enable valid inference of the causal relation to the domain (the theoretical population). In a case-control study, a sampling of the control group should be taken from the same study population (study base), have similar profiles to the cases (matching) but do not have the outcome seen in the cases. Matching important factors minimizes the confounding of the factors and increases statistical efficiency by ensuring similar numbers of cases and controls in confounders’ strata [ 23 - 24 ]. Nonetheless, perfect matching is neither necessary nor achievable in a case-control study because a partial match could achieve most of the benefits of the perfect match regarding a more precise estimate of odds ratio than statistical control of confounding in unmatched designs [ 25 - 26 ]. Moreover, perfect or full matching can lead to an underestimation of the point estimates [ 27 - 28 ].

Time feature: The timing of data collection for the determinant and outcome characterizes the types of studies. A cross-sectional study has the axis of time zero (T = 0) for both the determinant and the outcome, which separates it from all other types of research that have time for the outcome T > 0. Retrospective or prospective studies refer to the direction of data collection. In retrospective studies, information on the determinant and outcome have been collected or recorded before. In prospective studies, this information will be collected in the future. These terms should not be used to describe the relationship between the determinant and the outcome in etiologic studies. Time of exposure to the determinant, the time of induction, and the time at risk for the outcome are important aspects to understand. Time at risk is the period of time exposed to the determinant risk factors. Time of induction is the time from the sufficient exposure to the risk or causal factors to the occurrence of a disease. The latent period is when the occurrence of a disease without manifestation of the disease such as in “silence” diseases for example cancers, hypertension and type 2 diabetes mellitus which is detected from screening practices. Figure ​ Figure3 3 illustrates the time features of a variable. Variable timing is important for accurate data capture. 

The Design of Statistical Analysis

Statistical analysis of epidemiologic data provides the estimate of effects after correcting for biases (e.g., confounding factors) measures the variability in the data from random errors or chance [ 7 , 16 , 29 ]. An effect estimate gives the size of an association between the studied variables or the level of effectiveness of an intervention. This quantitative result allows for comparison and assessment of the usefulness and significance of the association or the intervention between studies. This significance must be interpreted with a statistical model and an appropriate study design. Random errors could arise in the study resulting from unexplained personal choices by the participants. Random error is, therefore, when values or units of measurement between variables change in non-concerted or non-directional manner. Conversely, when these values or units of measurement between variables change in a concerted or directional manner, we note a significant relationship as shown by statistical significance. 

Variability: Researchers almost always collect the needed data through a sampling of subjects/participants from a population instead of a census. The process of sampling or multiple sampling in different geographical regions or over different periods contributes to varied information due to the random inclusion of different participants and chance occurrence. This sampling variation becomes the focus of statistics when communicating the degree and intensity of variation in the sampled data and the level of inference in the population. Sampling variation can be influenced profoundly by the total number of participants and the width of differences of the measured variable (standard deviation). Hence, the characteristics of the participants, measurements and sample size are all important factors in planning a study.

Statistical strategy: Statistical strategy is usually determined based on the theoretical and data collection designs. Use of a prespecified statistical strategy (including the decision to dichotomize any continuous data at certain cut-points, sub-group analysis or sensitive analyses) is recommended in the study proposal (i.e., protocol) to prevent data dredging and data-driven reports that predispose to bias. The nature of the study hypothesis also dictates whether directional (one-tailed) or non-directional (two-tailed) significance tests are conducted. In most studies, two-sided tests are used except in specific instances when unidirectional hypotheses may be appropriate (e.g., in superiority or non-inferiority trials). While data exploration is discouraged, epidemiological research is, by nature of its objectives, statistical research. Hence, it is acceptable to report the presence of persistent associations between any variables with plausible underlying mechanisms during the exploration of the data. The statistical methods used to produce the results should be explicitly explained. Many different statistical tests are used to handle various kinds of data appropriately (e.g., interval vs discrete), and/or the various distribution of the data (e.g., normally distributed or skewed). For additional details on statistical explanations and underlying concepts of statistical tests, readers are recommended the references as cited in this sentence [ 30 - 31 ]. 

Steps in statistical analyses: Statistical analysis begins with checking for data entry errors. Duplicates are eliminated, and proper units should be confirmed. Extremely low, high or suspicious values are confirmed from the source data again. If this is not possible, this is better classified as a missing value. However, if the unverified suspicious data are not obviously wrong, they should be further examined as an outlier in the analysis. The data checking and cleaning enables the analyst to establish a connection with the raw data and to anticipate possible results from further analyses. This initial step involves descriptive statistics that analyze central tendency (i.e., mode, median, and mean) and dispersion (i.e., (minimum, maximum, range, quartiles, absolute deviation, variance, and standard deviation) of the data. Certain graphical plotting such as scatter plot, a box-whiskers plot, histogram or normal Q-Q plot are helpful at this stage to verify data normality in distribution. See Figure ​ Figure4 4 for the statistical tests available for analyses of different types of data.

Once data characteristics are ascertained, further statistical tests are selected. The analytical strategy sometimes involves the transformation of the data distribution for the selected tests (e.g., log, natural log, exponential, quadratic) or for checking the robustness of the association between the determinants and their outcomes. This step is also referred to as inferential statistics whereby the results are about hypothesis testing and generalization to the wider population that the study’s sampled participants represent. The last statistical step is checking whether the statistical analyses fulfill the assumptions of that particular statistical test and model to avoid violation and misleading results. These assumptions include evaluating normality, variance homogeneity, and residuals included in the final statistical model. Other statistical values such as Akaike information criterion, variance inflation factor/tolerance, and R2 are also considered when choosing the best-fitted models. Transforming raw data could be done, or a higher level of statistical analyses can be used (e.g., generalized linear models and mixed-effect modeling). Successful statistical analysis allows conclusions of the study to fit the data. 

Bayesian and Frequentist statistical frameworks: Most of the current clinical research reporting is based on the frequentist approach and hypotheses testing p values and confidence intervals. The frequentist approach assumes the acquired data are random, attained by random sampling, through randomized experiments or influences, and with random errors. The distribution of the data (its point estimate and confident interval) infers a true parameter in the real population. The major conceptual difference between Bayesian statistics and frequentist statistics is that in Bayesian statistics, the parameter (i.e., the studied variable in the population) is random and the data acquired is real (true or fix). Therefore, the Bayesian approach provides a probability interval for the parameter. The studied parameter is random because it could vary and be affected by prior beliefs, experience or evidence of plausibility. In the Bayesian statistical approach, this prior belief or available knowledge is quantified into a probability distribution and incorporated into the acquired data to get the results (i.e., the posterior distribution). This uses mathematical theory of Bayes’ Theorem to “turn around” conditional probabilities.

The goal of research reporting is to present findings succinctly and timely via conference proceedings or journal publication. Concise and explicit language use, with all the necessary details to enable replication and judgment of the study applicability, are the guiding principles in clinical studies reporting.

Writing for Reporting

Medical writing is very much a technical chore that accommodates little artistic expression. Research reporting in medicine and health sciences emphasize clear and standardized reporting, eschewing adjectives and adverbs extensively used in popular literature. Regularly reviewing published journal articles can familiarize authors with proper reporting styles and help enhance writing skills. Authors should familiarize themselves with standard, concise, and appropriate rhetoric for the intended audience, which includes consideration for journal reviewers, editors, and referees. However, proper language can be somewhat subjective. While each publication may have varying requirements for submission, the technical requirements for formatting an article are usually available via author or submission guidelines provided by the target journal. 

Research reports for publication often contain a title, abstract, introduction, methods, results, discussion, and conclusions section, and authors may want to write each section in sequence. However, best practices indicate the abstract and title should be written last. Authors may find that when writing one section of the report, ideas come to mind that pertains to other sections, so careful note taking is encouraged. One effective approach is to organize and write the result section first, followed by the discussion and conclusions sections. Once these are drafted, write the introduction, abstract, and the title of the report. Regardless of the sequence of writing, the author should begin with a clear and relevant research question to guide the statistical analyses, result interpretation, and discussion. The study findings can be a motivator to propel the author through the writing process, and the conclusions can help the author draft a focused introduction.

Writing for Publication

Specific recommendations on effective medical writing and table generation are available [ 32 ]. One such resource is Effective Medical Writing: The Write Way to Get Published, which is an updated collection of medical writing articles previously published in the Singapore Medical Journal [ 33 ]. The British Medical Journal’s Statistics Notes series also elucidates common and important statistical concepts and usages in clinical studies. Writing guides are also available from individual professional societies, journals, or publishers such as Chest (American College of Physicians) medical writing tips, PLoS Reporting guidelines collection, Springer’s Journal Author Academy, and SAGE’s Research methods [ 34 - 37 ]. Standardized research reporting guidelines often come in the form of checklists and flow diagrams. Table ​ Table6 6 presents a list of reporting guidelines. A full compilation of these guidelines is available at the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network website [ 38 ] which aims to improve the reliability and value of medical literature by promoting transparent and accurate reporting of research studies. Publication of the trial protocol in a publicly available database is almost compulsory for publication of the full report in many potential journals.

Graphics and Tables

Graphics and tables should emphasize salient features of the underlying data and should coherently summarize large quantities of information. Although graphics provide a break from dense prose, authors must not forget that these illustrations should be scientifically informative, not decorative. The titles for graphics and tables should be clear, informative, provide the sample size, and use minimal font weight and formatting only to distinguish headings, data entry or to highlight certain results. Provide a consistent number of decimal points for the numerical results, and with no more than four for the P value. Most journals prefer cell-delineated tables created using the table function in word processing or spreadsheet programs. Some journals require specific table formatting such as the absence or presence of intermediate horizontal lines between cells.

Decisions of authorship are both sensitive and important and should be made at an early stage by the study’s stakeholders. Guidelines and journals’ instructions to authors abound with authorship qualifications. The guideline on authorship by the International Committee of Medical Journal Editors is widely known and provides a standard used by many medical and clinical journals [ 39 ]. Generally, authors are those who have made major contributions to the design, conduct, and analysis of the study, and who provided critical readings of the manuscript (if not involved directly in manuscript writing). 

Picking a target journal for submission

Once a report has been written and revised, the authors should select a relevant target journal for submission. Authors should avoid predatory journals—publications that do not aim to advance science and disseminate quality research. These journals focus on commercial gain in medical and clinical publishing. Two good resources for authors during journal selection are Think-Check-Submit and the defunct Beall's List of Predatory Publishers and Journals (now archived and maintained by an anonymous third-party) [ 40 , 41 ]. Alternatively, reputable journal indexes such as Thomson Reuters Journal Citation Reports, SCOPUS, MedLine, PubMed, EMBASE, EBSCO Publishing's Electronic Databases are available areas to start the search for an appropriate target journal. Authors should review the journals’ names, aims/scope, and recently published articles to determine the kind of research each journal accepts for publication. Open-access journals almost always charge article publication fees, while subscription-based journals tend to publish without author fees and instead rely on subscription or access fees for the full text of published articles.

Conclusions

Conducting a valid clinical research requires consideration of theoretical study design, data collection design, and statistical analysis design. Proper study design implementation and quality control during data collection ensures high-quality data analysis and can mitigate bias and confounders during statistical analysis and data interpretation. Clear, effective study reporting facilitates dissemination, appreciation, and adoption, and allows the researchers to affect real-world change in clinical practices and care models. Neutral or absence of findings in a clinical study are as important as positive or negative findings. Valid studies, even when they report an absence of expected results, still inform scientific communities of the nature of a certain treatment or intervention, and this contributes to future research, systematic reviews, and meta-analyses. Reporting a study adequately and comprehensively is important for accuracy, transparency, and reproducibility of the scientific work as well as informing readers.

Acknowledgments

The author would like to thank Universiti Putra Malaysia and the Ministry of Higher Education, Malaysia for their support in sponsoring the Ph.D. study and living allowances for Boon-How Chew.

The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.

The materials presented in this paper is being organized by the author into a book.

Frequently Asked Questions: NIH Clinical Trial Definition

What is the difference between clinical research and a clinical trial.

Clinical trials are clinical research studies.

Clinical research includes all research involving human participants. It does not include secondary studies using existing biological specimens or data collected without identifiers or data that are publicly available.

Clinical trials are clinical research studies involving human participants assigned to an intervention in which the study is designed to evaluate the effect(s) of the intervention on the participant and the effect being evaluated is a health-related biomedical or behavioral outcome.

How can researchers determine whether a proposed study is a clinical trial?

The following questions should be used to determine whether a study meets the NIH clinical trial definition:

• Does the study involve human participants?
• Are the participants prospectively assigned to an intervention?
• Is the study designed to evaluate the effect of the intervention on the participants?
• Is the effect being evaluated a health-related biomedical or behavioral outcome?

If the answers are all “ yes ” the study is a clinical trial. If any answers are “ no ” the study is not a clinical trial.

Does the primary outcome of a study need to be a health-related outcome in order for a study to be considered a clinical trial?

If any outcome is health-related and the answers to the four questions are all yes, then the study is meets the clinical trial definition. You should note, though, that all NIH-funded research investigating biomedical or behavioral outcomes is considered to be health- related. Hence, if the outcome is biomedical or behavioral, the study may be a clinical trial (if the answers to the other three questions are “yes”). Many clinical trials are “mechanistic” or “exploratory” falling outside the realm of efficacy or effectiveness trials.

What is the difference between the clinical trial definition in the revised Common Rule and the NIH clinical trial definition?

NIH considers the two definitions to have the same meaning.

• Revised Common Rule § .102(b) : “Clinical trial means a research study in which one or more human subjects are prospectively assigned to one or more interventions (which may include placebo or other control) to evaluate the effects of the interventions on biomedical or behavioral health-related outcomes.”
• NIH clinical trial definition : “A research study in which one or more human subjects are prospectively assigned to one or more interventions4 (which may include placebo or other control) to evaluate the effects of those interventions on health-related biomedical or behavioral outcomes.” ( October 23, 2014)

Does risk to human participants factor into whether a study is considered to be a clinical trial?

Risk is not part of the NIH clinical trial definition. NIH considers the study to be a clinical trial as long as all elements of the NIH clinical trial definition are met.

What is the sub-definition of “intervention”?

An intervention is defined as a manipulation of the subject or subject’s environment for the purpose of modifying one or more health-related biomedical or behavioral processes and/or endpoints. Examples include: drugs/small molecules/compounds; biologics; devices; procedures (e.g., surgical techniques); delivery systems (e.g., telemedicine, face- to-face interviews); strategies to change health-related behavior (e.g., diet, cognitive therapy, exercise, development of new habits); treatment strategies; prevention strategies; and, diagnostic strategies.

Are measurements the same as interventions?

No; measurements are used to evaluate outcomes.

Does the NIH clinical trial definition apply to foreign awards?

Yes; the NIH clinical trial definition applies to all NIH-funded studies.

How will NIH educate researchers?

https://grants.nih.gov/policy/clinical-trials.htm .

Additionally, NIH staff are prepared to help educate researchers on whether their studies meet the NIH clinical definition.

Specific Cases

If a proposed clinical study includes a plan for addressing incidental findings, is the study considered to be a clinical trial.

No; having a plan for addressing incidental findings does not determine whether a study is considered to be a clinical trial. To determine whether your study meets the NIH clinical trial definition, please refer to the four questions above that outline the criteria.

Are studies that propose to evaluate a clinical intervention or to develop a diagnostic tool considered to be clinical trials?

It depends; studies that involve prospective assignment of human participants to an intervention, which may be a clinical intervention or development of a diagnostic tool, and that are designed to evaluate an effect of the intervention on the participant, where the effect is a biomedical or behavioral health outcome, are clinical trials. ( See examples in these Case Studies ). Studies designed only to validate the sensitivity or specificity of a tool are not clinical trials ( See examples in these Case Studies ).

Are studies that elicit the opinions or preferences from human participants considered to be clinical trials?

No; studies eliciting opinions or preferences are not considered to be health-related outcomes.

Are observational studies, which do not include an intervention, considered to be clinical trials?

No; in order to meet the NIH clinical trial definition there must be an intervention.

Are studies that involve only healthy participants considered to be clinical trials?

Yes; studies involving healthy participants are considered clinical trials if all elements of the NIH clinical trial definition are met.

Are studies that are not designed to impact diagnoses or treatment of patients considered to be clinical trials?

It depends; studies that meet all elements of the NIH clinical trial definition are considered to be clinical trials. ( See examples in these Case Studies )

Are studies designed to investigate whether a technique can be used to measure a response in research participants considered to be clinical trials?

Are studies designed to compare two approved diagnostic or therapeutic devices considered to be clinical trials.

No; a study must be designed to evaluate the effect of the intervention on the human participant to meet the NIH clinical trial definition.

Must a health-related outcome be permanent or lasting in order for a study to be a clinical trial?

No; a transient health-related outcome is sufficient for a study to be considered a clinical trial, as long as all other elements of the NIH clinical trial definition are met.

Are studies that coordinate with health-care providers where the outcome is measured in their patients considered to be clinical trials?

Yes; in these studies, both the health-care providers and patients are human participants, and the health care providers become part of the intervention. The study is considered to be a clinical trial as long as all other elements of the NIH clinical trial definition are met. ( See examples in these Case Studies )

Are studies with just a few research participants considered to be clinical trials?

Yes; the NIH clinical trial definition specifies that there must be one or more human participants involved in the study. The study is considered to be a clinical trial if all elements of the NIH clinical trial definition are met.

Are studies ancillary to clinical trials considered to be clinical trials as well?

Yes; studies ancillary to clinical trials are themselves are considered to be clinical trials if all elements of the NIH clinical trial definition are met.

Are studies that use correlational designs considered to be clinical trials?

No; studies using correlational designs to prospectively associate biomedical parameters with other health-related measures, but do not involve an intervention, do not meet the NIH clinical trial definition.

Are studies designed to understand a disease mechanism considered to be clinical trials?

Yes; studies that are designed to evaluate the effect of an intervention on a research participant, and meet all other elements of the clinical trial definition, meet the NIH clinical trial definition.

Are studies that compare two different methods of diagnosing a disease in patients to determine the reliability of a new method, but have no intention of using the results to inform the clinical care of the patients considered to be clinical trials?

No; studies that involve a comparison of methods and that do not evaluate the effect of the interventions on the participant do not meet the NIH clinical trial definition.

Are studies that evaluate the effect of an intervention on research participants, but do not have a comparison group (e.g., placebo, control) considered to be clinical trials?

Studies need not include a comparison group to meet the NIH clinical trial definition. As long as all of the elements of the NIH clinical trial definition are met, the study would be considered to be a clinical trial.

This page was last updated on Wednesday, December 13, 2023

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October 18, 2016

Understanding Clinical Studies

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Part of the challenge of explaining clinical research to the public is describing the important points of a study without going into a detailed account of the study’s design. There are many different kinds of clinical studies, each with their own strengths and weaknesses, and no real shorthand way to explain them. Researchers sometimes don’t explicitly state the kind of study they’re talking about. To them, it’s obvious; they’ve been living and breathing this research for years, sometimes decades. But study design can often be difficult even for seasoned health and science communicators to understand.

The gold standard for proving that a treatment or medical approach works is a well-designed randomized controlled trial. This type of study allows researchers to test medical interventions by randomly assigning participants to treatment or control groups. The results can help determine if there’s a cause-and-effect relationship between the treatment and outcomes. But clinical researchers can’t always use this approach. For example, scientists can’t ethically study risky behaviors by asking people to start smoking or eating an unhealthy diet. And they can’t study the health effects of the environment by assigning people to live in different places.

Thus, researchers must often turn to some type of observational study, in which a population’s health or behaviors are observed and analyzed. These studies can’t prove cause and effect, but they can be useful for finding associations. Observational studies can also help researchers understand a situation and come up with hypotheses that can then be put to the test in clinical trials. These types of studies have been essential to understanding the genetic, infectious, environmental, and behavioral causes of disease.

We’ve developed a one-page guide to clarify the different kinds of clinical studies researchers use, to explain why researchers might use them, and to touch a little on each type’s strengths and weaknesses. We hope it can serve as a useful resource to explain clinical research, whether you’re describing the results of a study to the public or the design of a trial to a potential participant. Please take a look and share your thoughts with us by sending an email to [email protected] .

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What Are Cancer Research Studies?

What is cancer research and why is it important.

Research is the key to progress against cancer and is a complex process involving professionals from many fields. It is also thanks to the participation of people with cancer, cancer survivors, and healthy volunteers that any breakthroughs go on to improve treatment and care for those who need it.

Cancer research studies may lead to discoveries such as new drugs to treat cancer, new therapies to make symptoms less severe, or lifestyle changes to reduce the chances of getting cancer.

Cancer research may also address big picture questions like why cancer is more prevalent in certain populations or how doctors can make existing cancer detection tools more effective in health care settings.

These discoveries can help people with cancer and their caregivers live fuller lives.

Who should join cancer research studies?

When you choose to participate in a research study, you become a partner in scientific discovery. Your generous contribution can make a world of difference for people like you.

As scientists continue to conduct cancer research, anyone can consider joining a research study. The best research includes everyone, and everyone includes you.

Your unique experience with cancer is incredibly valuable and may help current and future generations lead healthier lives.

When more people of all different races, ethnicities, ages, genders, abilities, and backgrounds participate, more people benefit.

It is important for scientists to capture the full genetic diversity of human populations so that the lessons learned are applicable to everyone.

What are the types of cancer research studies?

See below for definitions on the four major types of research and their subtypes:

• basic research
• quality of life/supportive care
• natural history
• longitudinal
• population-based
• epidemiological research
• translational research

Basic Research

Basic cancer research studies explore the very laws of nature. Scientists learn how cancer cells grow and divide, for example, by growing and testing bacteria , viruses , fungi , animal cells, and human cells in a lab. Scientists also study, for example, the genes that make up tumors in mice and rats in the lab. These experiments help build the foundation for further discovery.

Why Participate in a Clinical Trial?

Get information on how to evaluate a clinical trial and what questions to ask.

Clinical Research

Clinical research involves the study of cancer in people. These cancer research studies are further broken down into two types: clinical trials and observational studies .

• Treatment trials test how safe and useful a new treatment or way of using existing treatments is for people with cancer. Test treatments may include drugs, approaches to surgery or radiation therapy , or combinations of treatments.
• Prevention trials are for people who do not have cancer but are at a high risk for developing cancer or for cancer coming back. Prevention clinical trials target lifestyle changes (doing something) or focus on certain nutrients or medicines (adding something).
• Screening trials test how effective screening tests are for healthy people. The goal of these trials is to discover screening tools or methods that reduce deaths from cancer by finding it earlier.
• Quality-of-life/supportive care tests aim to help people with cancer, as well as their family and loved ones, cope with side effects like pain, nutrition problems, nausea and vomiting , sleeping problems, and depression . These trials may involve drugs or activities like therapy and exercising.  

Find Observational Studies

View a studies that are looking for people now.

• Natural history studies look at certain conditions in people with cancer or people who are at a high risk of developing cancer. Researchers often collect information about a person and their family medical history , as well as blood, saliva, and tumor samples. For example, a biomarker test may be used to get a genetic profile of a person’s cancer tissue. This may reveal how certain tumors change over the course of treatment .
• Longitudinal studies gather data on people or groups of people over time, often to see the result of a habit, treatment, or change. For example, two groups of people may be identified as those who smoke and those who do not. These two groups are compared over time to see whether one group is more likely to develop cancer than the other group.
• Population-based studies explore the causes of cancer, cancer trends, and factors that affect cancer care in specific populations. For example, a population-based study may explore the causes of a high cancer rate in a regional Native American population.

Epidemiological Research

Epidemiological research is the study of the patterns, causes, and effects of cancer in a group of people of a certain background. This research encompasses both observational population-based studies but also includes clinical epidemiological studies where the relationship between a population’s risk factors and treatments are tested.

Translational Research

Translational research is when cancer research moves across research disciplines, from basic lab research into clinical settings, and from clinical settings into everyday care. In turn, findings from clinical studies and population-based studies can inform basic cancer research. For example, data from the genetic profile of a tumor during an observational study may help scientists develop a clinical trial to test which drugs to prescribe to cancer patients with specific tumor genes.

Monica Bertagnolli, Director, NIH; former director, NCI; cancer survivor

Participation in Cancer Research Matters

I am so happy to have the opportunity to acknowledge the courage and generosity of an estimated 494,018 women who agreed to participate in randomized clinical trials with results reported between 1971 and 2018.

Their contributions showed that mammography can detect cancer at an early stage, that mastectomies and axillary lymph node dissections are not always necessary, that chemotherapy can benefit some people with early estrogen receptor–positive, progesterone receptor–positive, HER2-negative breast cancer but is not needed for all, and that hormonal therapy can prevent disease recurrence.

For just the key studies that produced these results, it took the strength and commitment of almost 500,000 women. I am the direct beneficiary of their contributions, and I am profoundly grateful.

The true number of brave souls contributing to this reduction in breast cancer mortality over the past 30 years? Many millions. These are our heroes.

— From NCI Director’s Remarks by then-NCI Director Monica M. Bertagnolli, M.D., at the American Society of Clinical Oncology Annual Meeting, June 3, 2023

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Clinical research: what is it.

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Webinar: NIH’s Definition of a Clinical Trial

Does your research study meet the National Institutes of Health (NIH) definition of a clinical trial? Watch this webinar recording to find out!

Experts from the National Institute of Mental Health (NIMH) provided an overview of NIH clinical trial classifications, with a particular focus on global mental health research. Their insight will help you correctly identify whether a study is considered a clinical trial so you can:

• Select the right NIH funding opportunity for your research study
• Write the research strategy and human subjects sections of your grant application and contract proposal
• Comply with appropriate policies and regulations

The webinar recording is appropriate for researchers and early career investigators.

Read the transcript .

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• Published: 12 August 2024

A roadmap towards improving outcomes in multiple myeloma

• Mohamad Mohty   ORCID: orcid.org/0000-0002-7264-808X 1 ,
• Thierry Facon 2 ,
• Florent Malard   ORCID: orcid.org/0000-0002-3474-0002 1 &
• Jean-Luc Harousseau 3  

Blood Cancer Journal volume  14 , Article number:  135 ( 2024 ) Cite this article

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Multiple myeloma (MM) is a chronic hematologic malignancy that remains incurable, because most patients eventually relapse or become refractory to current treatments. MM is a major health problem, with a globally increasing incidence. While, increase in the choice of MM treatment, including new immunotherapies (bispecific monoclonal antibodies and chimeric antigen receptor (CAR)-T cell therapy), may allow to further improve MM patients’ outcomes, some non-therapy-related key issues may represent a pre-requisite towards improving MM outcomes in the next few years. This includes, the necessity of real-world evidence data, of a better definition of frailty, of a dynamic disease risk assessment, of a better definition of high-risk disease, broader accessibility to novel drugs, and to ensure diversity and representation of underrepresented groups. These key issues will be discussed in the current perspective review.

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Incidence, mortality and survival in multiple myeloma compared to other hematopoietic neoplasms in Sweden up to year 2016

Introduction.

Multiple myeloma (MM) is a chronic hematologic malignancy that remains incurable because most patients eventually relapse or become refractory to treatment [ 1 ]. It is predominately diagnosed in people aged 65–75 years and is responsible for 10–15% of all hematologic malignancies, and in 2022, 21.9% of deaths in the United States related to hematologic cancers [ 2 ]. Due to an ageing population, the incidence is increasing. Basic research has made huge progress in the understanding of the molecular mechanisms of the disease, the immune system, and the tumor microenvironment, which has translated into the manufacture of revolutionary immunotherapies such as immunomodulatory drugs, monoclonal antibodies, bispecific antibodies, and chimeric antigen receptor (CAR)-T cell therapy. Dendritic vaccines (before and after autologous stem cell transplant [ASCT]) are starting to show promising results in high-risk patients [ 3 ]. However, this increase in the choice of treatment coupled with the fact that MM patients are a highly heterogeneous group, presents a huge challenge to clinicians who lack robust guidelines to select the most appropriate treatment for a given patient. While there are currently some hot debates about some specific therapeutic approaches (eg. quadruplet induction versus triplet, early transplant versus no transplant, immunotherapies for earlier relapse or frontline treatment, etc.), this perspective review aims to discuss a few non-therapy-related key issues which we believe would represent a pre-requisite towards improving MM outcomes in the next few years.

Role of real-world evidence

Knowledge from well-designed clinical trials must be combined with real-world (RW) data to improve therapeutic strategies and hence outcomes of MM patients everywhere. There may be great variation in prognostic patient and disease characteristics between clinical trial populations and RW cohorts, such differences may drive differences in outcomes. A large Canadian population-based cohort study has highlighted the significant efficacy-effectiveness gap between registrational randomized controlled trials and RW usage of treatment regimens, with MM patients treated in RW settings having death rates 75% higher than those in clinical trials [ 4 ]. Improvements in outcomes of patients with MM depend on the use of approved regimens; those based on efficacy results from large phase III randomized controlled trials (RCTs). Such trials are the gold standard for treatment outcomes; however, they must be unbiased in design and paramount to this is the selection of appropriately assessed endpoints. Patient selection for RCTs is problematic as many RW patients will not meet the stringent trial inclusion criteria and so the effectiveness of treatments in the RW setting will be unknown [ 5 ].

A better definition of frailty

Frail and/or elderly patients are rarely included in clinical trials [ 6 ], thus the benefits are less clear-cut in this patient group. However, existing frailty scores including that of the International Myeloma Working Group (IMWG) are problematic as they weigh age heavily, mis-categorizing the ‘fit elderly’ as frail [ 7 ]. Frailty prevalence varies greatly across trials ranging from 17 to 74% [ 7 ]. Not all frail people are old; are fit elderly patients the same as young frail patients? It is imperative that frail patients are identified more accurately with a standardized tool that is easy to use in the RW setting. A frailty score should encompass not only traditional clinical parameters but also the physical, psychological, and social aspects of a patient’s well-being. Frailty assessment tools must be easy to administer and although they are being increasingly incorporated into trial designs, there remains wide heterogeneity in the categorization and cut-off for frailty which will limit our ability to evaluate any associated outcomes. Recruitment of frail patients will allow a better understanding of treatment toxicity and determine if discontinuation is the real reason behind their poorer outcomes.

In their frailty-adjusted therapy study in transplant non-eligible newly diagnosed MM (NDMM) patients, the authors demonstrated the feasibility of recruiting older, less fit patients to clinical trials [ 8 ]. This phase III, multi-center, RCT investigated whether dose adjustments dependent on frailty would improve a patient’s ability to remain on therapy, reduce toxicity, and improve clinical outcomes. They showed that the IMWG frailty score demonstrated a dynamic biomarker potential both representing improved functionality in relation to disease response to therapy, as well as deterioration consequential to treatment-emergent toxicity. In addition, the concept of ultra-frail represents an opportunity to further stratify patients who may need additional support in order to improve their outcomes [ 8 ]. Further work in these two areas is needed. Dynamic frailty scoring would improve trial design as frailty is not a static concept, it may improve or deteriorate at each post diagnosis landmark interval. Frailty status at varying time points has also been shown to be a better predictor of outcomes than frailty status at time of diagnosis [ 9 ]. This limitation of evaluation of frailty-associated outcomes is an area that is largely unexplored and needs more attention.

Towards a dynamic disease risk assessment

First-line therapy for MM is still largely based on the eligibility of patients to undergo ASCT rather than on the biological characteristics of the disease itself or the depth of response achieved during therapy. Prognostic factors detected both before and throughout treatment may enable a more precise prediction of MM patients’ outcome that would allow personalized approaches. Cumulative evidence from trials has confirmed the robust association of minimal residual disease (MRD) status and survival outcomes in MM and has highlighted the primary importance of MRD in guiding treatment decisions. It can be accepted that MRD negativity should be a new endpoint in MM therapy (in addition to the classical other endpoints such as PFS and OS), regardless of cytogenetic risk, depth of response at MRD assessment, and the time of MRD measurement (NDMM or relapsed/refractory disease, and before or after maintenance therapy initiation) [ 10 ]. Therefore, it is essential that trials adopt standardized methodologies to assess MRD at specific time points. An exploratory analysis of the ongoing POLLUX and CASTOR studies in relapsed/refractory patients, found that sustained MRD negativity (defined as the maintenance of MRD negativity in bone marrow confirmed ≥6 or ≥12 months apart) is associated with improved progression-free survival (PFS) compared with patients who obtain MRD-negative status but not MRD durability [ 11 ]. This supports the concept that sustained MRD negativity may serve as a surrogate end point for PFS in ongoing and future clinical trials.

As a matter of fact, some uniformity is needed in how MRD is reported and at what threshold. Usually, MRD is measured at specific timepoints during therapy e.g., post-induction, +100 days post-ASCT, post-consolidation, pre-maintenance, and during maintenance. Data suggest that the duration of MRD negativity may be important, but little data are available on sustained MRD negativity (i.e., the need to confirm MRD at different timepoints) and on its optimal duration. Future trials should allow the exploration of different time cutoffs for sustained MRD negativity. In addition, one may wonder whether the sensitivity of the technique impact the reliability of MRD evaluation. The French IFM/DFCI 2009 trial [ 12 ] found that among 163 patients who were MRD-negative pre-maintenance using multiparameter flow cytometry (MFC) with a sensitivity of 10 −4 , 84 (56%) were indeed MRD-positive using next-generation sequencing (NGS) with a sensitivity of 10 −6 (3-year PFS, 86 vs . 66% in NGS-negative vs . NGS-positive among MFC-negative patients). To avoid unacceptable risk of undertreatment, clinical trials exploring treatment interruption based on MRD levels must use techniques of adequate and high sensitivity.

As a corollary, if MRD negativity is a major prognostic determinant, it would be important to question whether treatment administered and baseline risk stratification matter so long as MRD negativity is achieved. Regarding MM patients who are at high-risk according to baseline prognostic factors such as high-risk cytogenetics, MRD-negative patients evaluated at a low level of sensitivity (10 −4 ) still showed inferior clinical outcomes compared with standard-risk patients. Conversely, achieving MRD negativity at a sensitivity of 10 −5 to 10 −6 has been shown to overcome the inferior outcome observed in high-risk vs . standard-risk patients. MRD-driven clinical trials are needed to determine if treatment de-escalation or deintensification in MRD-negative patients is feasible without impairing patient prognosis [ 13 ].

Currently, there is no consensus on how or when to use the available ultrasensitive MRD assessment techniques for detecting and monitoring MRD status. Prospectively gathered clinical data will be useful in developing future paradigms for MRD analysis as a clinical practice decision tool. Future clinical trials must consider MRD negativity as an additional primary endpoint. Therefore, a second urgent need for the development and incorporation of MRD into clinical trials is in new drug development and registration. The development and approval of novel agents both for initial therapy and treatment of relapsed MM has already extended both PFS and overall survival (OS) several-fold. Therefore, at present, it is no longer possible to examine the impact of a novel agent to treat NDMM, alone or in combination, utilizing PFS and OS as endpoints, as these metrics would require clinical trials lasting well over a decade. Such a delayed determination of efficacy is unfair for patients and caregivers alike; moreover, it would slow drug development due to the prohibitive cost of such trials. There is therefore a crucial need for a parameter or surrogate marker, such as MRD, which can be examined earlier after treatment and predict subsequent PFS and OS. MRD negativity may not be an appropriate goal in all patient subsets (e.g., frail patients), but we may be able to inform the intensity or duration of therapy upon MRD status. Obviously, quality of life and patient-reported outcomes measurement can also be included in dynamic risk assessment, particularly in frail older patients where MRD may not always be a goal per se.

More widespread incorporation of MRD into clinical trials will allow us to determine whether patients should receive consolidation therapy to achieve MRD or if the duration of maintenance therapy can be defined by MRD status. The recent positive opinion of the Oncologic Drugs Advisory Committee (ODAC) of the FDA, is an important step in the right direction.

A better definition of high-risk disease

It is critical to establish a definition of high-risk disease in order to move towards risk-adapted treatment approaches. Defining risk at diagnosis is important to both effectively design future clinical trials and guide which clinical data is needed in routine practice, but the definition of high-risk disease is a challenge [ 14 ]. High-risk MM at diagnosis is currently recognized according to the Revised International Staging System (R-ISS) which was set up in 2015 [ 15 ] as a more accurate prognostic model for NDMM, incorporating ISS stage, serum lactate dehydrogenase (LDH), and high-risk cytogenetics assessed by interphase fluorescent in situ hybridization (FISH). High-risk cytogenetic abnormalities defined as the presence of del(17p) and/or t(4;14) and/or t(14;16) or an elevated LDH above the upper limit of normal are risk factors that upstage patients in the R-ISS system. More recently, the “R2-ISS” revised the R-ISS by analyzing the additive value of each single risk feature, including chromosome 1q gain/amplification (1q+). This R2-ISS proved to be a simple prognostic staging system allowing a better stratification of patients with intermediate-risk newly-diagnosed MM [ 16 ]. Since then, an expert consensus was reached regarding the opportunity to revise the R-ISS including chromosome 1 abnormality by FISH, TP53 mutation or deletion by NGS, amount of circulating plasma cells by NGFC (next-generation flow cytometry), and multiple extramedullary plasmacytomas at PET-CT (positron emission tomography–computed tomography; H. Avet-Loiseau, personal communication). Clinicians are waiting for results of prospective trials that integrate high-risk features and MRD in the decision algorithm.

Despite several validated risk stratification systems in clinical use, a uniform approach to risk in NDMM remains elusive. While we attempt to capture risk at diagnosis, the reality is that many important prognostic characteristics remain ill-defined, as some patients relapse early, while they were defined as low-risk based on their genomic profile at diagnosis. It is critical to establish a definition of high-risk disease in order to move towards risk-adapted treatment approaches. Current thinking is that defining risk at diagnosis is important to both effectively design future clinical trials and guide which clinical data is needed in routine practice [ 17 , 18 ]. The International Myeloma Society (IMS) has worked recently on a consensus on genomic definition of high-risk MM. The panel considered (i) del17p (in more than 20% of sorted plasma cells), (ii) TP53 mut (with no threshold for VAF), (iii) del(1p32) del/del , (iv) t(4;14) or t(14;16) or t(14;20) + gain/amp 1q or del(1p32) del/wt , and (v) gain/amp 1q + del(1p32) del/wt as markers of high-risk MM (J. Corr, personal communication). The routine application of the latter definition is likely to impact the field significantly.

Ensuring diversity and representation of underrepresented groups

MM patients of racial and ethnic minority are frequently underrepresented in clinical trials [ 19 ]. In the USA, it is estimated that approximately 20% of all patients with MM are of African American descent and yet, representation in clinical trials has historically been somewhere between 5 and 8%, and arguably even less in pivotal trials that have led to drug approval. There needs to be an understanding of the efficacy and [ 20 ] (eg. skin and nail toxicities in a black patient when using talquetamab) of these agents in different patient groups. For example, there are differences in the rates of cytokine release syndrome and neurological toxicities in patients of African descent and in Hispanic American patients. Thus, clinical trial teams should ensure that the materials are designed to be able to be read by and supported by a diverse population, that health care providers are trained in understanding culturally sensitive care, and that teams should include a diversity officer [ 21 ].

Recommendations on eliminating racial disparities in MM therapies have been made, including for pre- and post-approval of clinical trials, and relating to patient and industry perspectives [ 22 , 23 ]. There is positivity related to the latter in that clinical trial sponsors are now aware of such recommendations. Ultimately, an improved understanding of disparities in MM should translate to clinical trial designs to help to guide appropriate treatment choices and ensure that there is equitable treatment for all.

Establishing strategies to widen access to state-of-the art care

Supportive care is key component of optimal management of MM patients. The MM disease by itself is associated with a wide variety of debilitating complications, but also MM therapy is the source of many complications, including an increased risk of infection. All of these complications remain a frequent cause of morbidity and mortality. Therefore, more attention should be paid to these complications, and specific trials could focus on management of specific clinical situations (eg. prevention of infections, patients under dialysis, etc.) Vaccination is one of the most efficient methods to reduce morbidity and mortality. This is also true for MM. Screening for certain pathogens is recommended before starting and during aggressive therapies. Prophylactic medication with anti-virals, antibiotics, anti-fungals, immunoglobulin substitution can be recommended and should be validated in certain indications. In addition, adherence to high hygiene standards, including for example dietary recommendations, identifying, developing, and implementing evidence-based symptom management, or encouraging physical exercise, should be part of good clinical practice. The latter would likely improve patient QoL both in clinical trials and in routine practice.

Furthermore, one must acknowledge that not all drugs are available in all countries and even in the developed ones, certain drugs may not be available [ 24 , 25 ]. Pharmacoeconomic concerns are amplified in MM due to the need for multidrug regimens that combine two or more expensive new drugs, continuous therapy, and the prolonged disease course in most patients. The enormous costs of the combination of next-generation novel agents with immune antibody-based therapies must be addressed in order to assure access to these therapies for patients worldwide. First, trials should be able to identify drugs that work well in each subtype, but more importantly from a cost standpoint, we also need trials that can identify drugs that are unlikely to work in a given cytogenetic or risk subtype. Also, we need to determine whether an equivalent degree of survival benefit can be obtained with a short course of therapy compared with the current approach of prolonged therapy for many years. Approximately 15% of patients with NDMM can enjoy a long remission for a prolonged period of time with just lenalidomide and dexamethasone, or the latter combination plus ASCT [ 26 , 27 ]. It is likely that with some modern combinations, a subset of patients can be identified who can do well for many years following around one year of initial therapy. Finally, we need to determine whether we can adjust therapy based on response, so that patients who have achieved MRD-negativity can safely stop therapy, thereby providing improved value and quality of life [ 28 ].

Concluding remarks

The landscape of MM treatment is poised for transformation in 2024 and beyond. Embracing improved trial design, prioritizing patient-centric approaches, and fostering collaboration will be instrumental in achieving meaningful progress. By implementing the outlined action items, we can collectively propel MM research into a new era of innovation, ensuring that advancements translate into tangible improvements in patient outcomes.

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Acknowledgements

The authors would like to acknowledge the outstanding contribution, efforts and dedication of all stakeholders involved in the myeloma field.

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Patterns in use and tolerance of adjuvant neratinib in patients with hormone receptor (HR)-positive, HER2-positive early-stage breast cancer

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• Published: 17 August 2024

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• Julia Blanter 1 ,
• Elena Baldwin 2 ,
• Rima Patel 1 ,
• Tianxiang Sheng 1 &
• Amy Tiersten 1  

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One year of neratinib therapy is known to derive a significant invasive disease-free survival (iDFS) benefit in early-stage, hormone receptor-positive (HR +), HER2 + , node-positive breast cancer after trastuzumab-based adjuvant therapy. Limitations to neratinib use include significant gastrointestinal side effects, which often result in treatment discontinuation. In this study, we aimed to identify clinicopathologic features associated with adjuvant neratinib use and factors impacting treatment completion.

We performed a retrospective review of patients with early-stage HR + HER2 + breast cancer who were prescribed neratinib from 2017 to 2023 at our institution. We used the electronic medical record to extract information on patient characteristics, clinical features, and treatment information. Patients were identified as high risk based on definitions adapted from the standard high-risk definition in HR + HER2- breast cancer combined with studies correlating high Ki67 or high tumor grade with lower recurrence-free survival. Statistical analysis was performed using two-sided T-tests and chi-square tests.

We identified 62 eligible patients of whom 55% completed 1 year of neratinib and 45% did not. Sixty percent ( N  = 37) of patients offered neratinib were considered high risk at diagnosis. The most common reason for neratinib discontinuation was inability to tolerate side effects (54%) followed by pill burden (18%). The most common side effect experienced by patients was diarrhea despite anti-diarrheal prophylaxis (56%), followed by rash (8%). Patients who received an up-titration of neratinib were more likely to complete the full course of neratinib when compared to those who did not (76% vs. 40.5% p  = 0.013). The median starting dose of those who completed neratinib treatment was 140 vs. 240 mg in those who did not ( p  = 0.016). Neither group experienced a statistically significant greater likelihood of treatment holds or dose reductions. In terms of outcomes, 10 patients had progression of disease of whom 7 did not complete neratinib treatment ( p  = 0.169). Interestingly, those 7 patients developed metastatic disease and 57% ( N  = 4) had central nervous system metastases.

Patients are more likely to complete 1 year of adjuvant neratinib with dose up-titration. Dose reductions and interruptions did not affect neratinib adherence in our patient population. Seven patients (11%) in our study developed metastatic disease, all of whom did not complete adjuvant neratinib treatment.

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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Julia Blanter, Elena Baldwin, and Tianxiang Sheng. The first draft of the manuscript was written by Julia Blanter and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Blanter, J., Baldwin, E., Patel, R. et al. Patterns in use and tolerance of adjuvant neratinib in patients with hormone receptor (HR)-positive, HER2-positive early-stage breast cancer. Breast Cancer Res Treat (2024). https://doi.org/10.1007/s10549-024-07461-0

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DOI : https://doi.org/10.1007/s10549-024-07461-0

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