Using numerical modeling and simulation to assess the ethical burden in clinical trials and how it relates to the proportion of responders in a trial sample

Abstract

In order to explore how to reduce ethical losses in randomized controlled clinical trials (RCTs), we set out to identify trial participants, including non-responders, who do not contribute to demonstrating that the treatment in the experimental arm is superior to that in the control arm. RCTs emerged mid last century as the gold standard for assessing efficacy, becoming the cornerstone of the value of new therapies, yet their ethical grounds are still debated. We introduce the concept of unnecessary participants, defined as those included in RCTs considered not informative with respect to efficacy, in contrast to responders who carry all the information required to conclude on the treatment’s efficacy. Non-informative participants carry the burden of having to participate in a clinical trial without benefiting from it, which might include experiencing side effects. Non-informative participants can experience the outcome even if allocated to the experimental arm, or not present it even if allocated to the control arm. Thus, these unnecessary participants carry the ethical loss that is inherent to the RCT methodology. On the contrary, responders to the experimental treatment bear its entire efficacy in the RCT. Starting from the proportions observed in a real placebo-controlled trial from the literature, we carried out simulations of RCTs progressively increasing the proportion of responders up to 100%. We show that the number of unnecessary participants decreases steadily until the RCT’s ethical loss reaches a minimum. In parallel, the trial sample size decreases (presumably its cost as well), although the trial’s statistical power increases as shown by the increase of the chi-square comparing the event rates between the two arms. Thus, we expect that increasing the proportion of responders in RCTs would contribute to making them more ethically acceptable, with less false negative outcomes.