Simultaneous inference for longitudinal data with detection limits and covariates measured with errors, with application to AIDS studies
Article first published online: 18 MAY 2004
Copyright © 2004 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 23, Issue 11, pages 1715–1731, 15 June 2004
How to Cite
Wu, L. (2004), Simultaneous inference for longitudinal data with detection limits and covariates measured with errors, with application to AIDS studies. Statist. Med., 23: 1715–1731. doi: 10.1002/sim.1748
- Issue published online: 18 MAY 2004
- Article first published online: 18 MAY 2004
- Manuscript Accepted: OCT 2003
- Manuscript Received: MAY 2002
- Canada Natural Sciences and Engineering Research Council. Grant Number: 22R80742
- EM algorithm;
- Gibbs sampler;
- mixed-effects model
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. Copyright © 2004 John Wiley & Sons, Ltd.