E-values for Adaptive Clinical Trials: Anytime-Valid Monitoring in Practice

TL;DR

This paper introduces e-values and e-processes as flexible, anytime-valid statistical tools for adaptive clinical trials, enabling robust interim analyses, futility monitoring, and integration with existing methods, supported by practical guidance and software implementation.

Contribution

It develops the betting-martingale construction of e-processes for two-arm trials, demonstrating their advantages and providing practical guidance for their use in adaptive trial monitoring.

Findings

E-values handle composite null hypotheses effectively.

Calibrated group sequential rules outperform naive methods in power.

E-value methods maintain validity under continuous monitoring.

Abstract

Adaptive clinical trials rely on interim analyses, flexible stopping, and data-dependent design modifications that complicate statistical guarantees when fixed-horizon test statistics are repeatedly inspected or reused after adaptations. E-values and e-processes provide anytime-valid tests and confidence sequences that remain valid under optional stopping and optional continuation without requiring a prespecified monitoring schedule. This paper is a methodology guide for practitioners. We develop the betting-martingale construction of e-processes for two-arm randomized controlled trials, show how e-values naturally handle composite null hypotheses and support futility monitoring, and provide guidance on when e-values are appropriate, when established alternatives are preferable, and how to integrate e-value monitoring with group sequential and Bayesian adaptive workflows. A numerical study compares five monitoring rules -- naive and calibrated versions of frequentist, Bayesian, and e-value approaches -- in a two-arm binary-endpoint trial. Naive repeated testing and naive posterior thresholds inflate Type I error substantially under frequent interim looks. Among the valid methods, the calibrated group sequential rule achieves the highest power, the e-value rule provides robust anytime-valid control with moderate power, and the calibrated Bayesian rule is the most conservative. Extended simulations show that the power gap between group sequential and e-value methods depends on the monitoring schedule and reverses under continuous monitoring. The methodology, including futility monitoring, platform trial multiplicity control, and hybrid strategies combining e-values with established methods, is implemented in the open-source R package `evalinger` and situated within the regulatory framework of the January 2026 FDA draft guidance on Bayesian methodology.