Linear and nonlinear mixed-effects models for censored HIV viral loads using normal/independent distributions

Victor H. Lachos, Dipankar Bandyopadhyay, Dipak K. Dey

Research output: Contribution to journalArticlepeer-review

51 Scopus citations

Abstract

HIV RNA viral load measures are often subjected to some upper and lower detection limits depending on the quantification assays. Hence, the responses are either left or right censored. Linear (and nonlinear) mixed-effects models (with modifications to accommodate censoring) are routinely used to analyze this type of data and are based on normality assumptions for the random terms. However, those analyses might not provide robust inference when the normality assumptions are questionable. In this article, we develop a Bayesian framework for censored linear (and nonlinear) models replacing the Gaussian assumptions for the random terms with normal/independent (NI) distributions. The NI is an attractive class of symmetric heavy-tailed densities that includes the normal, Student's-t, slash, and the contaminated normal distributions as special cases. The marginal likelihood is tractable (using approximations for nonlinear models) and can be used to develop Bayesian case-deletion influence diagnostics based on the Kullback-Leibler divergence. The newly developed procedures are illustrated with two HIV AIDS studies on viral loads that were initially analyzed using normal (censored) mixed-effects models, as well as simulations.

Original languageEnglish (US)
Pages (from-to)1594-1604
Number of pages11
JournalBiometrics
Volume67
Issue number4
DOIs
StatePublished - Dec 2011

Keywords

  • Censored data
  • Gibbs sampler
  • HIV viral load
  • Influential observations
  • Linear mixed models
  • MCMC
  • Normal/independent distributions

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