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Statistics in medicine
Nov 30, 2023;42 (27): 4972-4989.

Semiparametric multivariate joint model for skewed-longitudinal and survival data: A Bayesian approach.

Jiaqing Chen, Yangxin Huang, Qing Wang
Author Information
  1. Jiaqing Chen: Department of Statistics, College of Science, Wuhan University of Technology, Wuhan, China.
  2. Yangxin Huang: College of Public Health, University of South Florida, Tampa, Florida, USA. ORCID
  3. Qing Wang: Yunnan Key Laboratory of Statistics Modeling and Data Analysis, Yunnan University, Kunming, China.
PMID: 37668072 DOI: 10.1002/sim.9896

Abstract

Joint models and statistical inference for longitudinal and survival data have been an active area of statistical research and have mostly coupled a longitudinal biomarker-based mixed-effects model with normal distribution and an event time-based survival model. In practice, however, the following issues may standout: (i) Normality of model error in longitudinal models is a routine assumption, but it may be unrealistically violating data features of subject variations. (ii) Data collected are often featured by the mixed types of multiple longitudinal outcomes which are significantly correlated, ignoring their correlation may lead to biased estimation. Additionally, a parametric model specification may be inflexible to capture the complicated patterns of longitudinal data. (iii) Missing observations in the longitudinal data are often encountered; the missing measures are likely to be informative (nonignorable) and ignoring this phenomenon may result in inaccurate inference. Multilevel item response theory (MLIRT) models have been increasingly used to analyze the multiple longitudinal data of mixed types (ie, continuous and categorical) in clinical studies. In this article, we develop an MLIRT-based semiparametric joint model with skew-t distribution that consists of an extended MLIRT model for the mixed types of multiple longitudinal data and a Cox proportional hazards model, linked through random-effects. A Bayesian approach is employed for joint modeling. Simulation studies are conducted to assess performance of the proposed models and method. A real example from primary biliary cirrhosis clinical study is analyzed to estimate parameters in the joint model and also evaluate sensitivity of parameter estimates for various plausible nonignorable missing data mechanisms.

Keywords

Bayesian analysis Markov chain Monte Carlo longitudinal-survival data multilevel item response theory multivariate joint model skew-t distribution

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MeSH Term

Humans
HIV Infections
Models, Statistical
Bayes Theorem
Longitudinal Studies
Viral Load
Journal Article

Word Cloud

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