Find the optimal graphical multiple testing procedure for
confirmatory clinical trials that test several hypotheses across
endpoints, doses, or subpopulations. Choose the graph maximising an explicit
gain-function: a user-defined measure of trial success that scores each
possible set of rejected hypotheses. Given simulated p-values, search over
hypothesis weights and transition weights to maximise expected trial
success. Conventional power criteria are recovered and the family-wise error
rate is strongly controlled due to the search being confined to graphical
procedures. Described in Spiers, Grayling, Wheeler, and Mander (2026)
"Gain-function optimisation of graphical multiple testing procedures for
confirmatory clinical trials"