Rare disease trials face unique statistical challenges due to limited patient populations and heterogeneous clinicalmanifestationsamongpatients. Multipleendpointsareoftennecessaryto comprehensively capture treatment benefits. Aglobaltest is an approachfor evaluating whether a treatment has any beneficial effect across multiple endpoints. We propose a new global test based on a weighted composite endpoint. The proposed global test employs shrinkage-based cross-validated targeted maximum likelihood estimation (CV-TMLE) to learn data-adaptive weights that maximize power while maintaining Type I error control. Shrinkage can be tailored to incorporateexistingdomainknowledge,suchasanticipatedrelativeeffectsizes. Insimulation studies designed to reflect real rare disease trial settings, the proposed procedure demonstrated improved power over standard multiplicity adjustments and classical global tests (such as the O’Brien test), while maintaining nominal Type I error, when effects are heterogeneous across endpoints. The proposed method simultaneously learns an optimal weighted composite outcomeandprovidesanunbiasedand efficient targetedmaximum likelihoodestimator(TMLE) for the average treatment effect (ATE) on that weighted outcome, with valid inference taking into account that the ATE is data dependent.
Keywords: CV-TMLE; global null hypothesis testing; multiple endpoints; rare disease clinical trials; data-adaptive target parameters