An MM Algorithm for the Frailty-Based Illness Death Model with Semi-Competing Risks Data

• Published in: Mathematics (Basel) Vol. 10; no. 19; p. 3702
• Main Authors: Huang, Xifen, Xu, Jinfeng, Guo, Hao, Shi, Jianhua, Zhao, Wenjie
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022
• Subjects: Algorithms; Causes of; colon cancer data; Data analysis; Death; Frailty; Illnesses; marginal likelihood; Methods; MM algorithm; Parameter estimation; Risk assessment; semi-competing risk gamma frailty model; surrogate function; China
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math10193702

For analyzing multiple events data, the illness death model is often used to investigate the covariate–response association for its easy and direct interpretation as well as the flexibility to accommodate the within-subject dependence. The resulting estimation and inferential procedures often depend on the subjective specification of the parametric frailty distribution. For certain frailty distributions, the computation can be challenging as the estimation involves both the nonparametric component and the parametric component. In this paper, we develop efficient computational methods for analyzing semi-competing risks data in the illness death model with the general frailty, where the Minorization–Maximization (MM) principle is employed for yielding accurate estimation and inferential procedures. Simulation studies are conducted to assess the finite-sample performance of the proposed method. An application to a real data is also provided for illustration.