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

2026-05-04

AI Summary

This study addresses the challenge of maintaining statistical power while controlling Type I error in clinical trials for rare diseases, where small sample sizes and heterogeneous clinical manifestations complicate multi-endpoint analyses. The authors propose a novel global testing framework based on a weighted composite endpoint, which integrates cross-validated targeted maximum likelihood estimation (CV-TMLE) with adaptive weight learning. This approach uniquely allows incorporation of domain knowledge while preserving valid inference under data-driven weighting. By employing a shrinkage strategy within CV-TMLE to optimize endpoint weights, the method constructs a weighted composite endpoint and performs a global test across multiple outcomes. Simulation studies demonstrate that, under effect heterogeneity, the proposed method substantially improves statistical power compared to conventional multiplicity adjustments and classical approaches such as O’Brien’s method, all while rigorously controlling the Type I error rate.

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.

Problem

• rare disease
• multiple endpoints
• global test
• statistical power
• heterogeneous effects

Innovation

• CV-TMLE
• global test
• weighted composite endpoint
• rare disease trials
• data-adaptive weighting