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    Politically Incorrect Diversity in Clinical Trials

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    Yes, you read that headline right. Intrigued? Good, but I have misled you just a little, as this paper is about Clinical Trial Diversity, but is not so much “politically incorrect” as “politically agnostic”. What I hope to accomplish in this paper is to briefly define the problem, answer the question “why should I care?” (Hint—it has nothing to do with political correctness), and some actions you may wish to consider for your next trial or submission.


    Recently, I was asked to sit on a panel at the Octane MedTech Innovation Forum in sunny Irvine California October 2021. I was certainly happy to escape the 40-degree Minnesota Autumn, but perhaps not so overjoyed at the topic—Diversity in Clinical Trials. In my current role, I increasingly see a push for a diverse population in In Vitro Diagnostic (IVD) trials, but little, if any action in Medical Device trials. With respect to drugs and biologics, my company at least has major initiatives, training, and task forces on the topic. Perhaps the difference between IVDs and devices may be a function of the size of the IVD trials (the last one we were involved in was 10,000 patients), and Device trials tend to be a small fraction of that—in the low hundreds, and sometimes less than 100—but then again, perhaps the IVD people have cottoned on to something device has yet to fully realize, let alone embrace. What’s that? I contend, as I did on the panel, that it is not about being politically correct or “woke”, but rather it should be all about the science. If we are admonished to “follow the science” in other aspects of life, why not in our clinical trials, as after all, is that not what we’re supposed to be doing? Besides that, there are compelling longstanding ethical reasons to pursue representative diversity in clinical trials.

    This is not a new concept. In 2002, nearly 20 years ago, The New England Journal of Medicine published an editorial by T.E. King MD whose opening lines read

    ”The rational use of a new drug or treatment should be based on the results of controlled clinical trials that are well designed, avoid bias, and include subjects representing the full range of patients who are likely to receive the treatment once it is marketed. In addition to age, sex, diet, underlying disease, and the concomitant use of other medications, race and genetic factors may play pivotal parts in the variability of subjects' responses to a medication.”1

    As King observed two decades ago, despite the efforts of various government agencies “it is uncertain…whether the participation of minority groups in clinical trials has increased”. As we will see below, the situation remains unacceptable, but there are concrete measures that can be taken to effect meaningful change.

    The problem

    Fundamentally, our clinical trials to date have not been representative of the populations in which the therapy is used. This state of affairs is fundamentally bad science—you cannot expect to see the same results in a diverse heterogenous population that were seen in a relatively homogenous population. It is very simple: if you wish to extrapolate the results of a trial to the entire population who will use the drug or device (or IVD for that matter), then the sample (clinical trial participants) must be representative of that population. To fail to do this is a fundamental sampling error that may lead manufacturers to conclude their product is safe and efficacious when in fact it may not be for the intended population. Much has been made in the past about how good results seen in approval trials are not mirrored when the therapy is released “into the wild” or the general population—and whilst some of that may be related to difference in performance at research sites, off-label use, patient non-compliance, etc., perhaps some of the disparity might just result from initial sampling error.2 Why? Simply because the inclusion and exclusion criteria (I/E) are constructed in such a fashion as to control variables and ensure a degree of homogeneity in the trial population. From a purely scientific perspective, there’s nothing wrong with this—it’s actually good scientific methodology—but it does mean the population studied is not the same as the broader population who may meet one or more of the exclusion criteria: from the perspective of generalizability to the entire population, many approvals trial I/E criteria actually create a sampling error situation.

    If we consider published data from Moderna on the COVE COVID-19 study in the US (and this is one of the more diverse studies where a conscious effort was made to represent population racial makeup appropriately)), we can see that 63% of patients were Caucasian, 20% Hispanic, 10% Black, 4% Asian and 3% other.3 US Census Bureau 2019 estimates for the same population breakout nationally are 60.3% Caucasian, 18.5% Hispanic, 13.4% Black and 5.9% Asian—other, including Alaskan Natives, Pacific Islanders etc. account for 4.3%4 We can see in this simple example that Caucasians were over-represented in the COVE study, whilst Blacks and Asians were quite under-represented (by 34% and 47.5% respectively). US FDA’s numbers for new drugs or biologics seeking approval in 2019 puts the Caucasian participation rate at 73%, with most other groups under-represented.5 For specific therapy types, the situation is even more unbalanced – Nazha et al report on nine advanced-solid-tumor phase III trials comprising 1711 patients—Caucasian participation ranged from 77.2% to 97.5% whilst Black participation ranged from 0-4%.6 These disturbing figures are in the face of a known 28% higher cancer-related mortality rate in Blacks compared to Caucasians.7 In another type of cancer, Caucasian females have a 131.8/100,000 incidence of breast cancer and Blacks 124.7, yet Blacks are 39.7% more likely to die from the disease, despite the lower incidence rate.8 It would seem that appropriate representation in clinical trials should be a priority to ensure therapies developed are effective in the populations that need them the most.

    Other examples abound in a variety of therapeutic areas. Just recently a meta-analysis published in May 2021 of over 33,400 open-angle glaucoma clinical trial participant data showed 70.7% of the study population was Caucasian, 16.8% was Black, 3.4% was Hispanic/Latino, and 9.1% consisted of other races/ethnicities, including Asian, Native Hawaiian or Pacific Islander, American Indian or Alaska Native, and unreported.9 For the sake of brevity, however, given the wide play this issue has been receiving in various news media in recent times, perhaps we can just take it as read that there are significant problems in the makeup of many clinical trials in terms of representation of the population in whom the therapy will be employed.

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