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We introduce a new multiple type I error criterion for clinical trials withmultiple populations. Such trials are of interest in precision medicine wherethe goal is to develop treatments that are targeted to specific sub-populationsdefined by genetic and/or clinical biomarkers. The new criterion is based onthe observation that not all type I errors are relevant to all patients in theoverall population. If disjoint sub-populations are considered, no multiplicityadjustment appears necessary, since a claim in one sub-population does notaffect patients in the other ones. For intersecting sub-populations we suggestto control the average multiple type error rate, i.e. the probably that arandomly selected patient will be exposed to an inefficient treatment. We callthis the population-wise error rate, exemplify it by a number of examples andillustrate how to control it with an adjustment of critical boundaries oradjusted p-values. We furthermore define corresponding simultaneous confidenceintervals. We finally illustrate the power gain achieved by passing fromfamily-wise to population-wise error rate control with two simple examples anda recently suggest multiple testing approach for umbrella trials.

A liberal type I error rate for studies in precision medicine