Influence of human immunodeficiency virus infection on neurological impairment: an analysis of longitudinal binary data with informative drop-out

• Published in: Applied statistics Vol. 48; no. 1; pp. 103 - 115
• Main Authors: Liu, X., Waternaux, C., Petkova, E.
• Format: Journal Article
• Language: English
• Published: Oxford, UK and Boston, USA Blackwell Publishers Ltd 1999; Blackwell Publishers; Blackwell
• Subjects: Applications; Binary outcome; Conditional probabilities; Estimation bias; Exact sciences and technology; HIV; Illness; Joint likelihood; Longitudinal data; Mathematics; Maximum likelihood estimation; Maximum likelihood estimators; Medical sciences; Missing data; Modeling; Multivariate analysis; Neurology; Parametric models; Probability and statistics; Ratio test; Sciences and techniques of general use; Statistical analysis; Statistics; Transition model; Virus; Missing data; Response function; Data analysis; Likelihood ratio test; Retroviridae; Binary data; Human immunodeficiency virus; Neurological disorder; Lentivirus; Mean square error; Biased estimation
• ISSN: 0035-9254; 1467-9876
• DOI: 10.1111/1467-9876.00143

A study to investigate the effect of human immunodeficiency virus (HIV) status on the course of neurological impairment, conducted by the HIV Center at Columbia University, followed a cohort of HIV positive and negative gay men for 5 years and assessed the presence or absence of neurological impairment every 6 months. Almost half of the subjects dropped out before the end of the study for reasons that might have been related to the missing neurological data. We propose likelihood-based methods for analysing such binary longitudinal data under informative and noninformative drop-out. A transition model is assumed for the binary response, and several models for the drop-out processes are considered which are functions of the response variable (neurological impairment). The likelihood ratio test is used to compare models with informative and non-informative drop-out mechanisms. Using simulations, we investigate the percentage bias and mean-squared error (MSE) of the parameter estimates in the transition model under various assumptions for the drop-out. We find evidence for informative drop-out in the study, and we illustrate that the bias and MSE for the parameters of the transition model are not directly related to the observed drop-out or missing data rates. The effect of HIV status on the neurological impairment is found to be statistically significant under each of the models considered for the drop-out, although the regression coefficient may be biased in certain cases. The presence and relative magnitude of the bias depend on factors such as the probability of drop-out conditional on the presence of neurological impairment and the prevalence of neurological impairment in the population under study.