Competing risks regression for clustered survival data via the marginal additive subdistribution hazards model

TL;DR

This paper introduces a population-averaged additive subdistribution hazards model for clustered competing risks data, providing robust estimation and goodness-of-fit tests, demonstrated through simulations and real-world data analysis.

Contribution

It extends existing models by accommodating dependent censoring and correlation within clusters, offering robust variance estimation and model assessment tools.

Findings

The proposed estimator performs well in finite samples.

The variance estimator is robust to misspecification.

Application to STRIDE trial data demonstrates practical utility.

Abstract

A population-averaged additive subdistribution hazards model is proposed to assess the marginal effects of covariates on the cumulative incidence function and to analyze correlated failure time data subject to competing risks. This approach extends the population-averaged additive hazards model by accommodating potentially dependent censoring due to competing events other than the event of interest. Assuming an independent working correlation structure, an estimating equations approach is outlined to estimate the regression coefficients and a new sandwich variance estimator is proposed. The proposed sandwich variance estimator accounts for both the correlations between failure times and between the censoring times, and is robust to misspecification of the unknown dependency structure within each cluster. We further develop goodness-of-fit tests to assess the adequacy of the additive structure of the subdistribution hazards for the overall model and each covariate. Simulation studies are conducted to investigate the performance of the proposed methods in finite samples. We illustrate our methods using data from the STrategies to Reduce Injuries and Develop confidence in Elders (STRIDE) trial.