Non-Parametric Estimation for Semi-Competing Risks Data With Event Misascertainment

作     者：Wu, Ruiqian Zhang, Ying Bakoyannis, Giorgos 

作者机构：Univ Nebraska Med Ctr Dept Biostat Omaha NE 68198 USA Indiana Univ Dept Biostat & Hlth Data Sci Indianapolis IN USA 

出 版 物：《STATISTICS IN MEDICINE》 (Stat. Med.)

年 卷 期：2025年第44卷第3-4期

页      面：e10332页

核心收录：

学科分类：0710[理学-生物学] 1004[医学-公共卫生与预防医学(可授医学、理学学位)] 1001[医学-基础医学(可授医学、理学学位)] 0714[理学-统计学（可授理学、经济学学位）] 10[医学] 

基　　金：National Institute of General Medical Sciences [U54GM115458, R01AG017177] National Institute of Health 

主　　题：EM algorithm illness-death model missing cause of failure non-parametric pseudo-likelihood estimation semi-competing risks 

摘      要：The semi-competing risks data model is a special type of disease-state model that focuses on studying the association between an intermediate event and a terminal event and proves to be a useful tool in modeling disease progression. The study of the semi-competing risk data model not only allows us to evaluate whether a disease episode is related to death but also provides a toolkit to predict death, given that the episode occurred at a certain time. However, the computation of the semi-competing risk models is a numerically challenging task. The Gamma-Frailty conditional Markov model has been shown to be an efficient computation model for studying semi-competing risks data. Building on recent advances in studying semi-competing risks data, this work proposes a non-parametric pseudo-likelihood method equipped with an EM-like algorithm to study semi-competing risks data with event misascertainment under the restricted Gamma-Frailty conditional Markov model. A thorough simulation study is conducted to demonstrate the inference validity of the proposed method and its numerical stability. The proposed method is applied to a large HIV cohort study, EA-IeDEA, that has a severe death under-reporting issue to assess the degree of adverse impact of the interruption of ART care on HIV mortality.