Nonparametric estimation of the total treatment effect with multiple outcomes in the presence of terminal events

TL;DR

This paper introduces a nonparametric method to estimate the total treatment effect on multiple outcomes, accounting for terminal events, with an accessible R package for practical application.

Contribution

It develops the AUMCF, a clinically interpretable, nonparametric estimator that accounts for terminal events and allows covariate adjustment, enhancing treatment effect analysis.

Findings

AUMCF effectively accounts for terminal events in multiple outcome analysis.

The augmentation estimator improves efficiency over unadjusted methods.

Application to clinical trial data demonstrates practical utility.

Abstract

As standards of care advance, patients are living longer and once-fatal diseases are becoming manageable. Clinical trials increasingly focus on reducing disease burden, which can be quantified by the timing and occurrence of multiple non-fatal clinical events. Most existing methods for the analysis of multiple event-time data require stringent modeling assumptions that can be difficult to verify empirically, leading to treatment efficacy estimates that forego interpretability when the underlying assumptions are not met. Moreover, many methods do not appropriately account for informative terminal events, such as premature treatment discontinuation or death, which prevent the occurrence of subsequent events. To address these limitations, we derive and validate estimation and inference procedures for the area under the mean cumulative function (AUMCF), an extension of the restricted mean survival time to the multiple event-time setting. The AUMCF is clinically interpretable, properly accounts for terminal competing risks, and can be estimated nonparametrically. To enable covariate adjustment, we also develop an augmentation estimator that provides efficiency at least equaling, and often exceeding, the unadjusted estimator. The utility and interpretability of the AUMCF are illustrated with extensive simulation studies and through an analysis of multiple heart-failure-related endpoints using data from the Beta-Blocker Evaluation of Survival Trial (BEST) clinical trial. Our open-source R package MCC makes conducting AUMCF analyses straightforward and accessible.