Title | Flexible longitudinal linear mixed models for multiple censored responses data. |
Publication Type | Journal Article |
Year of Publication | 2019 |
Authors | Lachos, Victor H., Larissa A. Matos, Luis M. Castro, and Ming-Hui Chen |
Journal | Stat Med |
Volume | 38 |
Issue | 6 |
Pagination | 1074-1102 |
Date Published | 2019 Mar 15 |
ISSN | 1097-0258 |
Keywords | Algorithms, HIV Infections, Humans, Likelihood Functions, Limit of Detection, Linear Models, Longitudinal Studies, Multivariate Analysis, Polymerase Chain Reaction, Time Factors, Viral Load |
Abstract | 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. |
DOI | 10.1002/sim.8017 |
Alternate Journal | Stat Med |
Original Publication | Flexible longitudinal linear mixed models for multiple censored responses data. |
PubMed ID | 30421470 |
PubMed Central ID | PMC6377307 |
Grant List | R01 GM070335 / GM / NIGMS NIH HHS / United States P01 CA142538 / CA / NCI NIH HHS / United States |