A CV-TMLE global test approach to improve power in rare disease clinical studies with multiple-component endpoints

TL;DR

This paper introduces a novel global test for rare disease trials that uses CV-TMLE to adaptively weight multiple endpoints, improving statistical power while controlling Type I error.

Contribution

It develops a new weighted composite endpoint global test employing shrinkage-based CV-TMLE to enhance power in heterogeneous endpoint settings.

Findings

Demonstrated improved power over standard tests in simulations.

Maintains nominal Type I error despite heterogeneity.

Learns optimal weights adaptively for better detection of treatment effects.

Abstract

Rare disease trials face unique statistical challenges due to limited patient populations and heterogeneous clinical manifestations among patients. Multiple endpoints are often necessary to comprehensively capture treatment benefits. A global test is an approach for 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 incorporate existing domain knowledge, such as anticipated relative effect sizes. In simulation 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 outcome and provides an unbiased and efficient targeted maximum likelihood estimator (TMLE) for the average treatment effect (ATE) on that weighted outcome, with valid inference taking into account that the ATE is data dependent.