Extending multivariate-t linear mixed models for multiple longitudinal data with censored responses and heavy tails

Wan Lun Wang, Tsung I. Lin, Victor H. Lachos

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)

Abstract

The analysis of complex longitudinal data is challenging due to several inherent features: (i) more than one series of responses are repeatedly collected on each subject at irregularly occasions over a period of time; (ii) censorship due to limits of quantification of responses arises left- and/or right- censoring effects; (iii) outliers or heavy-tailed noises are possibly embodied within multiple response variables. This article formulates the multivariate-t linear mixed model with censored responses (MtLMMC), which allows the analysts to model such data in the presence of the above described features simultaneously. An efficient expectation conditional maximization either (ECME) algorithm is developed to carry out maximum likelihood estimation of model parameters. The implementation of the E-step relies on the mean and covariance matrix of truncated multivariate-t distributions. To enhance the computational efficiency, two auxiliary permutation matrices are incorporated into the procedure to determine the observed and censored parts of each subject. The proposed methodology is demonstrated via a simulation study and a real application on HIV/AIDS data.

Original languageEnglish
Pages (from-to)48-64
Number of pages17
JournalStatistical Methods in Medical Research
Volume27
Issue number1
DOIs
Publication statusPublished - 2018 Jan 1

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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