Multivariate longitudinal data analysis with censored and intermittent missing responses

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

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)


The multivariate linear mixed model (MLMM) has emerged as an important analytical tool for longitudinal data with multiple outcomes. However, the analysis of multivariate longitudinal data could be complicated by the presence of censored measurements because of a detection limit of the assay in combination with unavoidable missing values arising when subjects miss some of their scheduled visits intermittently. This paper presents a generalization of the MLMM approach, called the MLMM-CM, for a joint analysis of the multivariate longitudinal data with censored and intermittent missing responses. A computationally feasible expectation maximization–based procedure is developed to carry out maximum likelihood estimation within the MLMM-CM framework. Moreover, the asymptotic standard errors of fixed effects are explicitly obtained via the information-based method. We illustrate our methodology by using simulated data and a case study from an AIDS clinical trial. Experimental results reveal that the proposed method is able to provide more satisfactory performance as compared with the traditional MLMM approach.

Original languageEnglish
Pages (from-to)2822-2835
Number of pages14
JournalStatistics in Medicine
Issue number19
Publication statusPublished - 2018 Aug 30

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Statistics and Probability


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