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

2024-12-12

AI Summary

Existing methods for analyzing recurrent event data often rely on unverifiable modeling assumptions and neglect the competing risks posed by informative terminal events (e.g., death, premature treatment discontinuation), leading to biased, non-robust, and poorly interpretable treatment effect estimates. To address this, we propose the Area Under the Mean Cumulative Function (AUMCF), the first nonparametric extension of restricted mean survival time to recurrent event settings, directly quantifying the overall treatment effect. We further develop a doubly robust covariate-adjusted augmented estimator that integrates inverse probability weighting with augmented inverse probability weighting (AIPW). The proposed methodology is implemented in the open-source R package *MCC*. Simulation studies and application to the BEST heart failure trial demonstrate AUMCF’s unbiasedness, stability, and clinical interpretability, substantially improving reliability, transparency, and competing-risk adjustment in multi-endpoint efficacy evaluation.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Calibration & Uncertainty Quantification
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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, most existing 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 nonparametric, clinically interpretable, and properly accounts for terminal competing risks. 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.

Problem

• Estimating treatment effects with multiple outcomes when terminal events occur
• Addressing limitations of existing methods requiring stringent modeling assumptions
• Accounting for informative terminal events that prevent subsequent outcomes

Innovation

• Nonparametric AUMCF estimator for multiple events
• Augmentation estimator for efficient covariate adjustment
• Handles terminal competing risks without assumptions