Recurrent events analysis in the presence of time-dependent covariates and dependent censoring

• 成果类型: Article
• 署名作者: Miloslavsky, M; Keles, S; van der Laan, MJ; Butler, S
• 署名单位: University of California System; University of California Berkeley; Roche Holding; Roche Holding USA; Genentech
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2004.00442.x
• 发表日期: 2004
• 页码: 239-257
• 关键词: cox regression-model; sample

摘要

Recurrent events models have had considerable attention recently. The majority of approaches show the consistency of parameter estimates under the assumption that censoring is independent of the recurrent events process of interest conditional on the covariates that are included in the model. We provide an overview of available recurrent events analysis methods and present an inverse probability of censoring weighted estimator for the regression parameters in the Andersen-Gill model that is commonly used for recurrent event analysis. This estimator remains consistent under informative censoring if the censoring mechanism is estimated consistently, and it generally improves on the naive estimator for the Andersen-Gill model in the case of independent censoring. We illustrate the bias of ad hoc estimators in the presence of informative censoring with a simulation study and provide a data analysis of recurrent lung exacerbations in cystic fibrosis patients when some patients are lost to follow-up.