Joint Models for Incomplete Longitudinal Data and Time-to-Event Data

Yuriko Takeda (), Toshihiro Misumi and Kouji Yamamoto
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Yuriko Takeda: Graduate School of Medicine, Yokohama City University, Yokohama 236-0004, Japan
Toshihiro Misumi: Department of Biostatistics, Yokohama City University School of Medicine, Yokohama 236-0004, Japan
Kouji Yamamoto: Department of Biostatistics, Yokohama City University School of Medicine, Yokohama 236-0004, Japan

Mathematics, 2022, vol. 10, issue 19, 1-7

Abstract: Clinical studies often collect longitudinal and time-to-event data for each subject. Joint modeling is a powerful methodology for evaluating the association between these data. The existing models, however, have not sufficiently addressed the problem of missing data, which are commonly encountered in longitudinal studies. In this paper, we introduce a novel joint model with shared random effects for incomplete longitudinal data and time-to-event data. Our proposed joint model consists of three submodels: a linear mixed model for the longitudinal data, a Cox proportional hazard model for the time-to-event data, and a Cox proportional hazard model for the time-to-dropout from the study. By simultaneously estimating the parameters included in these submodels, the biases of estimators are expected to decrease under two missing scenarios. We estimated the proposed model by Bayesian approach, and the performance of our method was evaluated through Monte Carlo simulation studies.

Keywords: missing data; joint model; missing at random; missing not at random; shared parameter model; longitudinal data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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