Flexible longitudinal linear mixed models for multiple censored responses data

• Published in: Statistics in medicine Vol. 38; no. 6; pp. 1074 - 1102
• Main Authors: Lachos, Victor H., A. Matos, Larissa, Castro, Luis M., Chen, Ming‐Hui
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 15.03.2019
• Subjects: Algorithms; censored data; Economic models; HIV Infections - virology; HIV viral load; Humans; Likelihood Functions; Limit of Detection; Linear Models; Longitudinal Studies; multiple longitudinal responses; Multivariate Analysis; outliers; Polymerase Chain Reaction; SAEM algorithm; Time Factors; Viral Load - statistics & numerical data; SAEM algorithm; censored data; outliers; multiple longitudinal responses; HIV viral load
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.8017

In biomedical studies and clinical trials, repeated measures are often subject to some upper and/or lower limits of detection. Hence, the responses are either left or right censored. A complication arises when more than one series of responses is repeatedly collected on each subject at irregular intervals over a period of time and the data exhibit tails heavier than the normal distribution. The multivariate censored linear mixed effect (MLMEC) model is a frequently used tool for a joint analysis of more than one series of longitudinal data. In this context, we develop a robust generalization of the MLMEC based on the scale mixtures of normal distributions. To take into account the autocorrelation existing among irregularly observed measures, a damped exponential correlation structure is considered. For this complex longitudinal structure, we propose an exact estimation procedure to obtain the maximum‐likelihood estimates of the fixed effects and variance components using a stochastic approximation of the EM algorithm. This approach allows us to estimate the parameters of interest easily and quickly as well as to obtain the standard errors of the fixed effects, the predictions of unobservable values of the responses, and the log‐likelihood function as a byproduct. The proposed method is applied to analyze a set of AIDS data and is examined via a simulation study.