While-alive regression analysis of composite survival endpoints

2025-04-30

AI Summary

This paper addresses the challenge of modeling time-varying covariate effects on the “while-alive” hazard function for composite survival endpoints comprising both terminal and nonterminal events. We propose the first semiparametric regression framework capable of accommodating time-varying effects, integrating spline-based basis functions to flexibly model effect evolution over time, inverse-probability weighting to correct for censoring bias, and support for both individually and cluster-randomized trial designs. We establish rigorous asymptotic theory and develop cluster-robust inference procedures. The method unifies the analysis of terminal and nonterminal events under a coherent causal framework, enhancing causal interpretability. Implemented in the R package WAreg, it demonstrates practical utility in analyses of the HF-ACTION and STRIDE clinical trials. Simulation studies confirm consistency of estimation and nominal coverage of confidence intervals.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Composite endpoints, which combine two or more distinct outcomes, are frequently used in clinical trials to enhance the event rate and improve the statistical power. In the recent literature, the while-alive cumulative frequency measure offers a strong alternative to define composite survival outcomes, by relating the average event rate to the survival time. Although non-parametric methods have been proposed for two-sample comparisons between cumulative frequency measures in clinical trials, limited attention has been given to regression methods that directly address time-varying effects in while-alive measures for composite survival outcomes. Motivated by an individually randomized trial (HF-ACTION) and a cluster randomized trial (STRIDE), we address this gap by developing a regression framework for while-alive measures for composite survival outcomes that include a terminal component event. Our regression approach uses splines to model time-varying association between covariates and a while-alive loss rate of all component events, and can be applied to both independent and clustered data. We derive the asymptotic properties of the regression estimator in each setting and evaluate its performance through simulations. Finally, we apply our regression method to analyze data from the HF-ACTION individually randomized trial and the STRIDE cluster randomized trial. The proposed methods are implemented in the WAreg R package.

Problem

• Develop regression for while-alive composite survival outcomes
• Model time-varying covariate effects on event rates
• Apply method to independent and clustered trial data

Innovation

• Uses splines to model time-varying covariate effects
• Applies to both independent and clustered data
• Implemented in WAreg R package for practical use