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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.)

Bayesian semiparametric modeling for HIV longitudinal data with censoring and skewness

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Author(s):
Castro, Luis M. [1] ; Wang, Wan-Lun [2] ; Lachos, Victor H. [3] ; de Carvalho, Vanda Inacio [4] ; Bayes, Cristian L. [5]
Total Authors: 5
Affiliation:
[1] Pontificia Univ Catolica Chile, Dept Stat, Casilla 306, Correo 22, Santiago - Chile
[2] Feng Chia Univ, Dept Stat, Grad Inst Stat & Actuarial Sci, Taichung - Taiwan
[3] Univ Connecticut, Dept Stat, Storrs, CT 06269 - USA
[4] Univ Edinburgh, Sch Math, Edinburgh, Midlothian - Scotland
[5] Pontificia Univ Catolica Peru, Dept Sci, Lima - Peru
Total Affiliations: 5
Document type: Journal article
Source: STATISTICAL METHODS IN MEDICAL RESEARCH; v. 28, n. 5, p. 1457-1476, MAY 2019.
Web of Science Citations: 1
Abstract

In biomedical studies, the analysis of longitudinal data based on Gaussian assumptions is common practice. Nevertheless, more often than not, the observed responses are naturally skewed, rendering the use of symmetric mixed effects models inadequate. In addition, it is also common in clinical assays that the patient's responses are subject to some upper and/or lower quantification limit, depending on the diagnostic assays used for their detection. Furthermore, responses may also often present a nonlinear relation with some covariates, such as time. To address the aforementioned three issues, we consider a Bayesian semiparametric longitudinal censored model based on a combination of splines, wavelets, and the skew-normal distribution. Specifically, we focus on the use of splines to approximate the general mean, wavelets for modeling the individual subject trajectories, and on the skew-normal distribution for modeling the random effects. The newly developed method is illustrated through simulated data and real data concerning AIDS/HIV viral loads. (AU)

FAPESP's process: 14/02938-9 - Estimation and diagnostics for censored mixed effects models using scale mixtures of skew-normal distributions
Grantee:Víctor Hugo Lachos Dávila
Support Opportunities: Regular Research Grants