Simultaneous inference for longitudinal data with detection limits and covariates measured with errors, with application to AIDS studies.
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.
Wu
Lang
L
Department of Statistics, University of British Columbia, Vancouver, BC, Canada V6T 1Z2. lang@stat.ubc.ca
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
D044966:Anti-Retroviral Agents