Autor(es)

Lachos, Victor H. ^[1] ; Bandyopadhyay, Dipankar ^[2] ; Dey, Dipak K. ^[3]

Afiliação do(s) autor(es)

^[1] Univ Estadual Campinas, Dept Stat, BR-6065 Sao Paulo - Brazil

^[2] Med Univ S Carolina, Div Biostat & Epidemiol, Charleston, SC 29425 - USA

^[3] Univ Connecticut, Dept Stat, Storrs, CT 06269 - USA

• Tipo de documento: Artigo Científico
• Fonte: BIOMETRICS; v. 67, n. 4, p. 1594-1604, DEC 2011.
• Citações Web of Science: 40

Resumo

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 KullbackLeibler 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. (AU)

• Processo FAPESP: 10/01246-5 - Modelos lineares e não lineares com distribuições de misturas de escala skew-normal
• Beneficiário: Víctor Hugo Lachos Dávila
• Modalidade de apoio: Bolsas no Exterior - Pesquisa