Mixture proportional hazards cure model with latent variables

• Published in: Statistics in medicine Vol. 40; no. 29; pp. 6590 - 6604
• Main Authors: He, Haijin, Han, Dongxiao, Song, Xinyuan, Sun, Liuquan
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 20.12.2021
• Subjects: Algorithms; Computer Simulation; cure model; factor analysis; Factor Analysis, Statistical; Humans; latent variables; Likelihood Functions; logistic regression; Models, Statistical; Normal Distribution; proportional hazards model; Proportional Hazards Models; Survival Analysis; factor analysis; cure model; latent variables; logistic regression; proportional hazards model
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.9200

A mixture proportional hazards cure model with latent variables is proposed. The proposed model assesses the effects of the observed and latent risk factors on the hazards of uncured subjects and the cure rate through a proportional hazards model and a logistic model, respectively. Factor analysis is employed to measure the latent variables through correlated multiple indicators. Maximum likelihood estimation is performed through a Gaussian quadratic technique that approximates the integration over the latent variables. A piecewise constant function is used for the unspecified baseline hazard of uncured subjects. The proposed method can be conveniently implemented by using SAS Proc NLMIXED. Simulation studies are conducted to evaluate the performance of the proposed approach. An application to a study concerning the risk factors of chronic kidney disease for type 2 diabetic patients is provided.