INFERRING THE EFFECT OF A RANDOMISED TREATMENT ON A RECURRENT EVENT PROCESS UNDER DEPENDENT CENSORING

• 成果类型: Article
• 署名作者: Waterschoot, Wout; Callegaro, Andrea; Moraschini, Luca; Vansteelandt, Stijn
• 署名单位: Ghent University; GlaxoSmithKline; GlaxoSmithKline Belgium; GlaxoSmithKline; GlaxoSmithKline Belgium
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157; 1941-7330
• DOI: 10.1214/25-AOAS2118
• 发表日期: 2026-03
• 页码: 25-45
• 关键词: recurrent event processes; dependent censoring; inverse probability of censoring weighting (IPCW); vaccine efficacy; semiparametric regression; INVERSE-PROBABILITY; repeated outcomes; longitudinal data; Causal Inference; models; survival

摘要

This work is motivated by randomized clinical trial NCT03281876 (November 2017-March 2020), whose secondary aim was to evaluate the efficacy of the NTHi-Mcat vaccine vs. placebo in preventing recurrent severe exacerbations among patients with acute exacerbations of chronic obstructive pulmonary disease (AECOPD). The published analysis (Vaccine 40 (2022) 5924-5932; Lancet Respir. Med. 10 (2022) 435-446) aimed to estimate the ratio of the expected number of exacerbations one experienced by the end of study in the vaccinated vs. the placebo arm. One, therefore, regressed the number of exacerbations one experienced by the last point in time one is uncensored on treatment and baseline covariates with offset the logarithm of the observation time to account for different follow-up times. In this paper we demonstrate that this approach is prone to selection bias due to: (i) selective withdrawal and (ii) selective timing of the outcome measurements. We show that inverse probability of censoring weigting (IPCW), a common approach to adjust for dependent censoring under the assumption that censoring is non-informative given the observed covariate history, does not suffice to restore the unbiasedness of the treatment effect estimator under the above-mentioned type of analysis. To address this, we propose hazard inverse probability of censoring weighting (HIPCW). This novel weighting technique preserves the simplicity of IPCW but extracts more efficiency by using each individual's last recorded outcome. We validate the proposed approach through extensive simulations and compare with: (i) IPCW at a single time, (ii) a variant of the existing IPCW-GEE routines (J. Amer. Statist. Assoc. 90 (1995) 106-121) and (iii) IPCW-based estimators derived from the popular Andersen-Gill model (J. R. Stat. Soc. Ser. B. Stat. Methodol. 66 (2004) 239-257). We illustrate the routines through reanalysing clinical trial NCT03281876.