Futility Analysis under Scrutiny

TL;DR

This paper examines how population shifts affect futility analyses in clinical trials, proposing post-stratification methods and a permutation test to improve decision accuracy amidst heterogeneity.

Contribution

It introduces post-stratification strategies and a permutation-based screening test to address population heterogeneity in futility analyses.

Findings

Post-stratification improves futility decision validity.

Naive, model-based, and hybrid methods vary in effectiveness.

Population heterogeneity can distort early stopping decisions.

Abstract

This paper investigates the robustness of futility analyses in clinical trials when interim analysis population deviates from the target population. We demonstrate how population shifts can distort early stopping decisions and propose post-stratification strategies to mitigate these effects. Simulation studies illustrate the impact of subgroup imbalances and the effectiveness of naive, model-based, and hybrid post-stratification methods. We also introduce a permutation-based screening test for identifying variables contributing to population heterogeneity. Our findings support the integration of post-stratification adjustments using all available baseline data at the interim analysis to enhance the validity and integrity of futility decisions.