Before a new drug is approved, it is tested on a group of volunteers in a clinical trial. Doctors then prescribe it to millions of patients, trusting that the results apply to everyone. But if the trial volunteers were mostly one type of person, such as white males, then we can’t be sure the drug works or is safe for everyone else. Too often, that is exactly what has happened. 

The Representation Gap in Clinical Trials 

Clinical trials have historically included more White, male, and younger participants than the broader populations affected by the conditions being studied. Women were routinely excluded from early-phase research for decades, until the 1993 NIH Revitalization Act required their inclusion in federally funded studies.

Race and ethnicity data show the same pattern. In recent years, analyses of clinical trial diversity data have found that roughly three-quarters of participants in pivotal trials supporting new drug approvals were White. Black participants made up around 8% of enrollees against about 13% of the US population, and Hispanic participants were around 11% against roughly 19%. The gap is even wider in cancer research. One large analysis of US cancer drug trials (Loree et al., JAMA Oncology, 2019) found that Black patients made up only about 3% of participants, even though Black Americans have higher death rates from several cancers. 

A 2026 analyst review of FDA-related enrollment data concluded that diversity in pivotal trials supporting US FDA approvals remains far from proportional despite a decade of federal transparency programs. Older adults are also underrepresented. Most cancer diagnoses occur after age 65, yet trials often enroll far younger patients, partly because eligibility criteria exclude people with other health conditions. 

Why It Matters Scientifically

This is not only a fairness issue. It’s a question of whether the science is valid.

Biology varies across populations. Genetic variants that affect how drugs are metabolised or tolerated are not evenly distributed. The HLA-B*1502 variant, more common in people of some Asian ancestries, raises the risk of severe skin reactions to carbamazepine. CYP2C19 poor-metabolizer variants, which reduce activation of the antiplatelet drug clopidogrel, are more frequent in East Asian populations. Warfarin dosing varies with CYP2C9 and VKORC1 variants that differ in frequency across ancestries. If a trial doesn’t include enough people carrying these variants, such differences are found late, sometimes only after patients are harmed.

Sex differences are real. In 2013, the FDA required a lower recommended dose of zolpidem (Ambien) for women after it emerged that women cleared the drug more slowly, leaving some impaired during the next morning’s activities. This case highlights the importance of evaluating sex-specific differences in drug metabolism and safety.

Devices can fail too. Pulse oximeters have been shown to overestimate blood oxygen levels in people with darker skin, which can mask dangerously low readings. The problem went unrecognised for years, in part because the devices weren’t adequately validated across skin tones.

Disease burden isn’t equal. Black Americans have higher rates of hypertension, diabetes, and Alzheimer’s disease than White Americans, among other conditions. Excluding the groups most affected means evidence is weakest where it is needed most.

Why the Gap Persists

The common explanation is that minority patients don’t want to participate, often tied to historical abuses such as the Tuskegee syphilis study. Distrust is real, but the evidence suggests it’s not the main driver. A meta-analysis by Wendler and colleagues (PLoS Medicine, 2006) found that minority groups were about as willing as White participants to enrol when actually asked. The deeper problem is that many are never asked.

• Eligibility criteria. Trials often exclude people with kidney problems or other conditions. Because these conditions are more common among Black and Hispanic patients and older adults, the exclusions remove more of them.
• Practical burden. Frequent visits mean time off work, childcare, and travel costs. Hourly workers, who are more likely to lack paid leave, are less able to afford this.
• Site selection. Trials run mostly at large academic hospitals in certain cities. Many communities have no site nearby, and the hospitals where minority patients are more often treated usually don’t run trials.

What Works

Evidence-backed approaches go beyond “try harder to recruit.”

1. Put trials where people are. Community health centres, rural clinics, and primary care practices can serve as trial sites, supported by partnerships with trusted local organizations.
2. Decentralise. Home health visits, local labs, telehealth follow-ups, and wearable monitoring reduce the travel burden, though they must be designed so they don’t exclude people without reliable internet access.
3. Broaden eligibility. Professional bodies such as ASCO and Friends of Cancer Research have recommended loosening exclusions that aren’t scientifically justified, such as blanket bans on certain comorbidities or prior malignancies.
4. Remove financial barriers. Reimbursing travel, childcare, and lost wages makes participation feasible for people with fewer resources.
5. Communicate clearly. Translated materials, plain-language consent, and community health workers or patient navigators build both understanding and trust.
6. Diversify the research workforce. Investigators and coordinators who share language and background with the community improve recruitment and retention.
7. Set goals and measure them. Establishing enrollment targets, monitoring participant demographics, and reviewing site-level data help sponsors identify recruitment gaps and address them promptly.

Representation Is Only the First Step: How Researchers Should Interpret the Evidence

Including participants from different backgrounds is essential, but representation alone cannot establish whether a treatment works equally well across all groups. Researchers must also collect reliable data and assess whether differences in safety, effectiveness, or drug exposure are clinically relevant.

Subgroup analyses need careful planning. Researchers may compare trial results across groups, such as different age groups, sexes, or racial and ethnic backgrounds, to identify potential differences in a study treatment response. Planning these analyses before a trial begins helps researchers determine which comparisons are relevant and whether the study includes enough participants to assess them reliably. When subgroup sizes are too small, the findings may still be inconclusive, making it difficult to determine whether observed differences reflect genuine study treatment effects or random variation.

Ultimately, the goal is not simply to meet enrollment targets. It is to generate evidence that clinicians and regulators can interpret with appropriate confidence, including a clear account of what is known, what remains uncertain, and which populations may need further study.

Conclusion:

Diversity in clinical trials is essential for building reliable evidence about study treatment safety and effectiveness across different populations. Addressing underrepresentation requires identifying and reducing barriers that prevent eligible people from participating, from unnecessary exclusion criteria to the costs and travel demands of study visits. By making trials more accessible and evaluating results across diverse population groups, researchers can strengthen the evidence and identify where further research is needed.

Metropolitan Cardiovascular Research aims to advance cardiovascular research by conducting clinical research studies that follow recognized quality standards and support the responsible conduct of clinical trials.