Multivariate longitudinal data analysis with censored and intermittent missing responses

Tsung-I Lin; Victor H Lachos; Wan-Lun Wang

doi:10.1002/sim.7692

Multivariate longitudinal data analysis with censored and intermittent missing responses

Stat Med. 2018 May 8. doi: 10.1002/sim.7692. Online ahead of print.

Authors

Tsung-I Lin^{1

2}, Victor H Lachos³, Wan-Lun Wang⁴

Affiliations

¹ Institute of Statistics, National Chung Hsing University, Taichung 402, Taiwan.
² Department of Public Health, China Medical University, Taichung 404, Taiwan.
³ Department of Statistics, University of Connecticut, Storrs, CT 06269, USA.
⁴ Department of Statistics, Graduate Institute of Statistics and Actuarial Science, Feng Chia University, Taichung 40724, Taiwan.

PMID: 29740829
DOI: 10.1002/sim.7692

Abstract

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.

Keywords: ECM algorithm; HIV AIDS study; censored data; missing-data imputation; truncated multivariate normal distribution.