🤖 AI Summary
This study addresses the challenges of bias from death-truncated recurrent events and the lack of doubly robust estimation in chronic disease trials. We propose a doubly robust estimator for the exposure-weighted while-alive rate based on a local Nelson-Aalen representation. Applicable to both individual and cluster-randomized trials, this method integrates augmented estimating equations with clustered influence function inference, establishing component-wise double robustness and asymptotic normality. Simulation studies and re-analyses of two clinical trials demonstrate that the proposed approach effectively eliminates survival truncation bias, enabling unbiased causal inference regarding event burden during survival. This work fills a critical methodological gap in handling informative censoring due to death in recurrent event analysis.
📝 Abstract
Randomized trials in chronic disease settings often measure treatment benefit through recurrent non-fatal events that are truncated by death, where conventional summaries either discard recurrences, conflate the treatment effect with survival, or treat death as censoring and forfeit a causal interpretation. While-alive estimands measure event burden per unit time alive, but doubly robust estimation for the exposure-weighted while-alive rate remains undeveloped, particularly in cluster-randomized trials (CRTs). We develop a doubly robust estimator based on a local Nelson-Aalen representation, with augmented estimating equations targeting the marginal hazard of the terminal event and the weighted recurrent event rate among those alive; the estimator accommodates multiple event types through prespecified clinical weights and remains consistent if either the censoring model or the outcome working models are correctly specified. For CRTs, we define a new pair of individual-average and cluster-average estimands under informative cluster size, with inference based on cluster-level influence functions. We establish component-wise double robustness and asymptotic normality, corroborate the theory in simulations, and illustrate the methods with reanalyses of data from two completed randomized trials.