title: Models for empirical Bayes estimators of longitudinal CD4 counts.
abstract: In this paper we consider the choice of model used in estimation of trajectories of CD4 T-cell counts by empirical Bayes estimators. Tsiatis et al. have demonstrated that empirical Bayes estimates of CD4 values correct for the bias resulting from measurement error when using CD4 as a covariate in a Cox model to predict clinical events. Here, empirical Bayes estimates from a random effects model are compared to estimates from the more general stochastic regression model presented in Taylor et al. Empirical Bayes estimators based on the two models are judged according to their ability to provide parameter estimates in a Cox model predicting clinical outcomes. Data from ACTG 118 are used as an illustration.
authors.affiliation: Department of Epidemiology and Biostatistics, Boston University 02118, USA.
authors.forename: M P
authors.initials: MP
authors.lastname: LaValley
authors.forename: V
authors.initials: V
authors.lastname: DeGruttola
mesh_terms: D000163:Acquired Immunodeficiency Syndrome / Q000188:drug therapy; D001499:Bayes Theorem; D018791:CD4 Lymphocyte Count / Q000187:drug effects*; D016049:Didanosine / Q000627:therapeutic use; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies; D015233:Models, Statistical; D016016:Proportional Hazards Models; D012044:Regression Analysis; D013269:Stochastic Processes
chemical_list: D016049:Didanosine