Flexible longitudinal linear mixed models for multiple censored responses data.

TitleFlexible longitudinal linear mixed models for multiple censored responses data.
Publication TypeJournal Article
Year of Publication2019
AuthorsLachos, Victor H., Larissa A. Matos, Luis M. Castro, and Ming-Hui Chen
JournalStat Med
Date Published2019 03 15
KeywordsAlgorithms, HIV Infections, Humans, Likelihood Functions, Limit of Detection, Linear Models, Longitudinal Studies, Multivariate Analysis, Polymerase Chain Reaction, Time Factors, Viral Load

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.

Alternate JournalStat Med
Original PublicationFlexible longitudinal linear mixed models for multiple censored responses data.
PubMed ID30421470
PubMed Central IDPMC6377307
Grant ListR01 GM070335 / GM / NIGMS NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States