Semiparametric latent class analysis of recurrent event data

• 成果类型: Article
• 署名作者: Zhao, Wei; Peng, Limin; Hanfelt, John
• 署名单位: Emory University; Shandong University
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12499
• 发表日期: 2022
• 页码: 1175-1197
• 关键词: mixture-models; longitudinal biomarker; REGRESSION-MODEL; stratification

摘要

Recurrent event data frequently arise in chronic disease studies, providing rich information on disease progression. The concept of latent class offers a sensible perspective to characterize complex population heterogeneity in recurrent event trajectories that may not be adequately captured by a single regression model. However, the development of latent class methods for recurrent event data has been sparse, typically requiring strong parametric assumptions and involving algorithmic issues. In this work, we investigate latent class analysis of recurrent event data based on flexible semiparametric multiplicative modelling. We derive a robust estimation procedure through novelly adapting the conditional score technique and utilizing the special characteristics of multiplicative intensity modelling. The proposed estimation procedure can be stably and efficiently implemented based on existing computational routines. We provide solid theoretical underpinnings for the proposed method, and demonstrate its satisfactory finite sample performance via extensive simulation studies. An application to a dataset from research participants at Goizueta Alzheimer's Disease Research Center illustrates the practical utility of our proposals.