Combining Epidemiologic and Biostatistical Tools to Enhance Variable Selection in HIV Cohort Analyses

• Published in: PloS one Vol. 9; no. 1; p. e87352
• Main Authors: Rentsch, Christopher, Bebu, Ionut, Guest, Jodie L., Rimland, David, Agan, Brian K., Marconi, Vincent
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 29.01.2014; Public Library of Science (PLoS)
• Subjects: Antiretroviral drugs; Antiretroviral Therapy, Highly Active; Armed forces; Bayes Theorem; Bayesian analysis; Bias; Biology; Cohort analysis; Cohort Studies; Consent; Criteria; Data Interpretation, Statistical; Defense programs; Drug therapy; Epidemiology; Female; Health sciences; HIV; HIV Infections - drug therapy; HIV Infections - mortality; Human immunodeficiency virus; Humans; Hypotheses; Hypothesis testing; Infectious diseases; Information theory; Kaplan-Meier Estimate; Male; Mathematical models; Mathematics; Medical centers; Medical diagnosis; Medical prognosis; Medical research; Medicine; Mortality; Multivariate Analysis; Proportional Hazards Models; Public health; Regression analysis; Regression models; Researchers; Significance; Statistical analysis; Statistical methods; Statistical significance; Studies; Survival; Survival analysis; Theory; Variables; Veterans; United States > US; Maryland; Georgia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0087352

Variable selection is an important step in building a multivariate regression model for which several methods and statistical packages are available. A comprehensive approach for variable selection in complex multivariate regression analyses within HIV cohorts is explored by utilizing both epidemiological and biostatistical procedures. Three different methods for variable selection were illustrated in a study comparing survival time between subjects in the Department of Defense's National History Study and the Atlanta Veterans Affairs Medical Center's HIV Atlanta VA Cohort Study. The first two methods were stepwise selection procedures, based either on significance tests (Score test), or on information theory (Akaike Information Criterion), while the third method employed a Bayesian argument (Bayesian Model Averaging). All three methods resulted in a similar parsimonious survival model. Three of the covariates previously used in the multivariate model were not included in the final model suggested by the three approaches. When comparing the parsimonious model to the previously published model, there was evidence of less variance in the main survival estimates. The variable selection approaches considered in this study allowed building a model based on significance tests, on an information criterion, and on averaging models using their posterior probabilities. A parsimonious model that balanced these three approaches was found to provide a better fit than the previously reported model.