Leveraging external data for testing experimental therapies with biomarker interactions in randomized clinical trials

In oncology the efficacy of novel therapeutics often differs across patient subgroups, and these variations are difficult to predict during the initial phases of the drug development process. The relation between the power of randomized clinical trials (RCTs) and heterogeneous treatment effects (HTEs) has been discussed by several authors. In particular, false negative results are likely to occur when the treatment effects concentrate in a subpopulation but the study design did not account for potential HTEs. The use of external data (ED) from completed clinical studies and electronic health records has the potential to improve decision-making throughout the development of new therapeutics, from early-stage trials to registration. Here we discuss the use of ED to evaluate experimental treatments with potential HTEs. We introduce a permutation procedure to test, at the completion of a RCT, the null hypothesis that the experimental therapy does not improve the primary outcomes in any subpopulation. The permutation test leverages the available ED to increase power. Also, the procedure controls the false positive rate at the desired α-level without restrictive assumptions on the ED, for example, in scenarios with unmeasured confounders, different pre-treatment patient profiles in the RCT population compared to the ED, and other discrepancies between the trial and the ED. We illustrate that the permutation test is optimal according to an interpretable criteria and discuss examples based on asymptotic results and simulations, followed by a retrospective analysis of individual patient-level data from a collection of glioblastoma clinical trials.