期刊
STATISTICAL METHODS IN MEDICAL RESEARCH
卷 28, 期 5, 页码 1457-1476出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/0962280218760360
关键词
Censored longitudinal data; HIV viral load; mixed-effects models; semiparametric regression; skewness
类别
资金
- Chilean government [FONDECYT 1170258]
- Programa Nacional de Innovacion para la Competitividad y Productividad (Innovate Peru) [452-PNICP-ECIP-2014]
- Department of Science of Pontificia Universidad Catolica del Peru
- Ministry of Science and Technology of Taiwan [MOST 105-2118-M-035-004-MY2]
- CNPq-Brazil [305054/2011-2]
- FAPESP-Brazil [2014/02938-9]
- FCT - Fundacao para a Ciencia e a Tecnologia, Portugal [UID/MAT/00006/2013]
- Direccion de Gestion de la Investigacion at PUCP [DGI-20140017/0070, DGI-2016-1-0077]
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.
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