title: Simultaneous inference for longitudinal data with detection limits and covariates measured with errors, with application to AIDS studies.
abstract: In AIDS studies such as HIV viral dynamics, statistical inference is often complicated because the viral load measurements may be subject to left censoring due to a detection limit and time-varying covariates such as CD4 counts may be measured with substantial errors. Mixed-effects models are often used to model the response and the covariate processes in these studies. We propose a unified approach which addresses the censoring and measurement errors simultaneously. We estimate the model parameters by a Monte-Carlo EM algorithm via the Gibbs sampler. A simulation study is conducted to compare the proposed method with the usual two-step method and a naive method. We find that the proposed method produces approximately unbiased estimates with more reliable standard errors. A real data set from an AIDS study is analysed using the proposed method.
authors.affiliation: Department of Statistics, University of British Columbia, Vancouver, BC, Canada V6T 1Z2. lang@stat.ubc.ca
authors.forename: Lang
authors.initials: L
authors.lastname: Wu
mesh_terms: D000163:Acquired Immunodeficiency Syndrome / Q000188:drug therapy* / Q000276:immunology / Q000821:virology; D000465:Algorithms; D044966:Anti-Retroviral Agents / Q000494:pharmacology; D018791:CD4 Lymphocyte Count; D002986:Clinical Trials as Topic / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D008137:Longitudinal Studies; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D019562:Viral Load
chemical_list: D044966:Anti-Retroviral Agents