Leveraging External Data for Testing Experimental Therapies with Biomarker Interactions in Randomized Clinical Trials

TL;DR

This paper proposes a permutation-based statistical method that uses external data to improve the detection of heterogeneous treatment effects in clinical trials, enhancing decision-making in drug development.

Contribution

It introduces a novel permutation test leveraging external data to assess treatment effects across subpopulations, controlling false positives without restrictive assumptions.

Findings

Permutation test increases power in detecting treatment effects.

Method controls false positive rate under data discrepancies.

Retrospective analysis demonstrates practical utility.

Abstract

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 and heterogeneous treatment effects 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 heterogeneous treatment effects. The use of external data 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 external data to evaluate experimental treatments with potential heterogeneous treatment effects. We introduce a permutation procedure to test, at the completion of a randomized clinical trial, the null hypothesis that the experimental therapy does not improve the primary outcomes in any subpopulation. The permutation test leverages the available external data to increase power. Also, the procedure controls the false positive rate at the desired $\alpha$-level without restrictive assumptions on the external data, for example, in scenarios with unmeasured confounders, different pre-treatment patient profiles in the trial population compared to the external data, and other discrepancies between the trial and the external data. 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.