LATENT CLASS ANALYSIS WITH DISCRETE FAILURE TIME MODEL

• 成果类型: Article
• 署名作者: Li, Qinmengge; He, Kevin; Tsoi, Lam C.; Kang, Jian
• 署名单位: University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157; 1941-7330
• DOI: 10.1214/25-AOAS2111
• 发表日期: 2026-03
• 页码: 346-363
• 关键词: survival analysis; Latent class; Finite mixture model; MIXTURE SURVIVAL MODEL; RENAL-TRANSPLANT; kidney-transplantation; TREATMENT MODALITIES; ESRD PATIENTS; Heterogeneity; BENEFIT; AGE; SURVEILLANCE; management

摘要

In survival analysis, accurate identification of latent classes is essential to effectively account for potential hidden population heterogeneity. In response to this challenge, we introduce the latent class discrete survival (LaCDS) model. LaCDS employs a finite-mixture model structure within the context of the discrete failure time model and implements the expectation-maximization algorithm for efficient optimization. Through extensive simulation studies, we evaluate the performance of LaCDS in comparison to other methods. Our results demonstrate LaCDS's superior ability to identify population heterogeneities, both in terms of baseline hazards and coefficients. Additionally, it is robust under both discrete and continuous simulation mechanisms. We apply LaCDS and other methods to identify subgroups among kidney transplant patients within the Organ Procurement and Transplantation Network (OPTN) study. Our findings underscore the superior accuracy of LaCDS in subgrouping homogeneous patients compared to existing methods.