A bivariate joint frailty model with mixture framework for survival analysis of recurrent events with dependent censoring and cure fraction. Issue 3 (7th January 2020)

• Record Type: Journal Article
• Authors: Tawiah, Richard; McLachlan, Geoffrey J.; Ng, Shu Kay
• Is Part Of: Biometrics. Volume 76:Issue 3(2020)
• Page Start: 753
• Page End: 766
• Publication Date: 2020-01-07
• Subjects: bivariate frailty -- cure proportion -- EM algorithm -- informative censoring -- joint model -- mixture model -- random effect -- terminal event; Biometry -- Periodicals; 570.15195
• DOI: 10.1111/biom.13202
• Languages: English
• ISSNs: 0006-341X

Abstract

Abstract: In the study of multiple failure time data with recurrent clinical endpoints, the classical independent censoring assumption in survival analysis can be violated when the evolution of the recurrent events is correlated with a censoring mechanism such as death. Moreover, in some situations, a cure fraction appears in the data because a tangible proportion of the study population benefits from treatment and becomes recurrence free and insusceptible to death related to the disease. A bivariate joint frailty mixture cure model is proposed to allow for dependent censoring and cure fraction in recurrent event data. The latency part of the model consists of two intensity functions for the hazard rates of recurrent events and death, wherein a bivariate frailty is introduced by means of the generalized linear mixed model methodology to adjust for dependent censoring. The model allows covariates and frailties in both the incidence and the latency parts, and it further accounts for the possibility of cure after each recurrence. It includes the joint frailty model and other related models as special cases. An expectation‐maximization (EM)‐type algorithm is developed to provide residual maximum likelihood estimation of model parameters. Through simulation studies, the performance of the model is investigated under different magnitudes of dependent censoring and cure rate. The model is applied to data sets from two colorectal cancer studies to illustrate its practical value.