Mixed-effects location and scale Tobit joint models for heterogeneous longitudinal data with skewness, detection limits, and measurement errors

Research output: Contribution to journalArticle

Abstract

The joint modeling of mean and variance for longitudinal data is an active research area. This type of model has the advantage of accounting for heteroscedasticity commonly observed in between and within subject variations. Most of researches focus on improving the estimating efficiency but ignore many data features frequently encountered in practice. In this article, we develop a mixed-effects location scale joint model that concurrently accounts for longitudinal data with multiple features. Specifically, our joint model handles heterogeneity, skewness, limit of detection, measurement errors in covariates which are typically observed in the collection of longitudinal data from many studies. We employ a Bayesian approach for making inference on the joint model. The proposed model and method are applied to an AIDS study. Simulation studies are performed to assess the performance of the proposed method. Alternative models under different conditions are compared.

Original languageEnglish
Pages (from-to)3525-3543
Number of pages19
JournalStatistical Methods in Medical Research
Volume27
Issue number12
DOIs
StatePublished - Dec 1 2018

Keywords

  • AIDS clinical trial
  • Bayesian inference
  • covariate measurement errors
  • limit of detection
  • longitudinal data
  • mixed-effects location scale models
  • skew-t distribution

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