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

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 alwa...

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Veröffentlicht in:Statistics in medicine Jg. 37; H. 18; S. 2771 - 2786
Hauptverfasser: Xu, Cong, Chinchilli, Vernon M., Wang, Ming
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Wiley Subscription Services, Inc 15.08.2018
Schlagworte:
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
Online-Zugang:Volltext
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Zusammenfassung: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.
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ISSN:0277-6715
1097-0258
1097-0258
DOI:10.1002/sim.7682