Academic Articles Pharmaceuticals

Treatment effect heterogeneity, self-selection into RCTs, and racial disparities

In drug development, clinical trials typically aim for a population that is representative of the patients who would be eligible for the treatment. While randomized controlled trials (RCTs) typically focus on measuring the average health impact of a drug across this population, a paper Basu and Gurjal (2020) argue that treatment effect heterogeneity and self-selection may lead to an exacerbation of racial disparities. They claim that:

…the presence of treatment effect heterogeneity across some measure of baseline risk, and noisy information about such heterogeneity, can induce self-selection into randomized clinical trials (RCTs) by patients with distributions of baseline risk different from that of the target population. Consequently, average results from RCTs can disproportionately affect the treatment choices of patients with different baseline risks. Using economic models for these sequential processes of RCT enrollment, information generation, and the resulting treatment choice decisions, we show that the dynamic consequences of such information flow and behaviors may lead to growth in disparities in health outcomes across racial and ethnic categories. These disparities arise due to either the differential distribution of risk across those categories at the time RCT results are reported or the different rate of change of baseline risk over time across race and ethnicity, even though the distribution of risk within the RCT matched that of the target population when the RCT was conducted. We provide evidence on how these phenomena may have contributed to the growth in racial disparity in diabetes incidence.

Specifically, the authors claim that this phenomenon of using everage treatment effects may explain “up to 10.5% of the recent growth in racial disparity in diabetes incidence in the United States.”

Certainly an interesting hypothesis and worth a read.


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