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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Skew-normal/independent linear mixed models for censored responses with applications to HIV viral loads

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Author(s):
Bandyopadhyay, Dipankar [1] ; Lachos, Victor H. [2] ; Castro, Luis M. [3] ; Dey, Dipak K. [4]
Total Authors: 4
Affiliation:
[1] Univ Minnesota, Sch Publ Hlth, Div Biostat, Minneapolis, MN 55455 - USA
[2] Univ Estadual Campinas, IMECC, Dept Estat, BR-13083859 Sao Paulo - Brazil
[3] Univ Concepcion, Dept Estadist, Concepcion - Chile
[4] Univ Connecticut, Dept Stat, Storrs, CT 06269 - USA
Total Affiliations: 4
Document type: Journal article
Source: BIOMETRICAL JOURNAL; v. 54, n. 3, p. 405-425, MAY 2012.
Web of Science Citations: 23
Abstract

Often in biomedical studies, the routine use of linear mixed-effects models (based on Gaussian assumptions) can be questionable when the longitudinal responses are skewed in nature. Skew-normal/elliptical models are widely used in those situations. Often, those skewed responses might also be subjected to some upper and lower quantification limits (QLs; viz., longitudinal viral-load measures in HIV studies), beyond which they are not measurable. In this paper, we develop a Bayesian analysis of censored linear mixed models replacing the Gaussian assumptions with skew-normal/independent (SNI) distributions. The SNI is an attractive class of asymmetric heavy-tailed distributions that includes the skew-normal, skew-t, skew-slash, and skew-contaminated normal distributions as special cases. The proposed model provides flexibility in capturing the effects of skewness and heavy tail for responses that are either left- or right-censored. For our analysis, we adopt a Bayesian framework and develop a Markov chain Monte Carlo algorithm to carry out the posterior analyses. The marginal likelihood is tractable, and utilized to compute not only some Bayesian model selection measures but also case-deletion influence diagnostics based on the KullbackLeibler divergence. The newly developed procedures are illustrated with a simulation study as well as an HIV case study involving analysis of longitudinal viral loads. (AU)

FAPESP's process: 11/17400-6 - Applications of the scale mixture of Skew-Normal distributions in linear mixed effects models
Grantee:Víctor Hugo Lachos Dávila
Support type: Regular Research Grants