A liberal type I error rate for studies in precision medicine

TL;DR

This paper proposes a new error control criterion for precision medicine trials that focuses on the average error rate across populations, allowing for more targeted and potentially more powerful statistical testing.

Contribution

It introduces the population-wise error rate for multiple testing in precision medicine, providing methods to control it and demonstrating power improvements over traditional approaches.

Findings

Population-wise error rate controls average patient error.

Methods for adjusting critical boundaries and p-values.

Power gains over family-wise error rate control.

Abstract

We introduce a new multiple type I error criterion for clinical trials with multiple populations. Such trials are of interest in precision medicine where the goal is to develop treatments that are targeted to specific sub-populations defined by genetic and/or clinical biomarkers. The new criterion is based on the observation that not all type I errors are relevant to all patients in the overall population. If disjoint sub-populations are considered, no multiplicity adjustment appears necessary, since a claim in one sub-population does not affect patients in the other ones. For intersecting sub-populations we suggest to control the average multiple type error rate, i.e. the probably that a randomly selected patient will be exposed to an inefficient treatment. We call this the population-wise error rate, exemplify it by a number of examples and illustrate how to control it with an adjustment of critical boundaries or adjusted p-values. We furthermore define corresponding simultaneous confidence intervals. We finally illustrate the power gain achieved by passing from family-wise to population-wise error rate control with two simple examples and a recently suggest multiple testing approach for umbrella trials.