Joint modeling of recurrent events and a terminal event adjusted for zero inflation and a matched design

• Published in: Statistics in medicine Vol. 37; no. 18; pp. 2771 - 2786
• Main Authors: Xu, Cong, Chinchilli, Vernon M., Wang, Ming
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 15.08.2018
• Subjects: Algorithms; Bayesian analysis; Biometry - methods; Computer Simulation; Death; frailty models; Humans; joint modeling; Likelihood Functions; Longitudinal Studies; Monte Carlo expectation‐maximization algorithm; Monte Carlo Method; Parameter estimation; recurrent events; Regression Analysis; zero inflation; joint modeling; recurrent events; death; Monte Carlo expectation-maximization algorithm; frailty models; zero inflation
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.7682

In longitudinal studies, matched designs are often employed to control the potential confounding effects in the field of biomedical research and public health. Because of clinical interest, recurrent time‐to‐event data are captured during the follow‐up. Meanwhile, the terminal event of death is always encountered, which should be taken into account for valid inference because of informative censoring. In some scenarios, a certain large portion of subjects may not have any recurrent events during the study period due to nonsusceptibility to events or censoring; thus, the zero‐inflated nature of data should be considered in analysis. In this paper, a joint frailty model with recurrent events and death is proposed to adjust for zero inflation and matched designs. We incorporate 2 frailties to measure the dependency between subjects within a matched pair and that among recurrent events within each individual. By sharing the random effects, 2 event processes of recurrent events and death are dependent with each other. The maximum likelihood based approach is applied for parameter estimation, where the Monte Carlo expectation‐maximization algorithm is adopted, and the corresponding R program is developed and available for public usage. In addition, alternative estimation methods such as Gaussian quadrature (PROC NLMIXED) and a Bayesian approach (PROC MCMC) are also considered for comparison to show our method's superiority. Extensive simulations are conducted, and a real data application on acute ischemic studies is provided in the end.