Minimax regret and monotone optimality in multiple testing

Abstract

Confirmatory trials may seek efficacy claims in a prespecified subgroup and the full popu- lation, although benefit outside the subgroup is uncertain. We measure power loss relative to the marginal level-α test available if the population to protect were known, using the same experiment. In a bivariate Gaussian model with known covariance, we minimise the largest power loss, or regret, over prespecified ranges of target and non-target effects, subject to strong familywise error control and coordinatewise monotonicity. We optimise over monotone closed- testing procedures with fixed marginal tests, allowing randomisation in the shared intersection test. Monotonicity places the worst non-target mean at its lower planning bound for each target effect. On the resulting reduced planning set, every least-favourable distribution is supported on exactly two efficacy scenarios, one for each target population. Under a condition on the effect ranges and covariance, the minimax procedure is nonrandomised and unique up to null sets. An integral criterion characterises exactly when a calibrated step-up procedure attains the minimax value within this class. Bounds for the continuous problem quantify possible improve- ments over simple procedures. An asthma planning example illustrates power allocation and the compatibility of target assignments with treatment benefits and burdens.