TY - JOUR
T1 - Bayesian semiparametric modeling for HIV longitudinal data with censoring and skewness
AU - Castro, Luis M.
AU - Wang, Wan Lun
AU - Lachos, Victor H.
AU - Inácio de Carvalho, Vanda
AU - Bayes, Cristian L.
N1 - Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: LM Castro acknowledges support from Grant FONDECYT 1170258 from the Chilean government, Programa Nacional de Innovación para la Competitividad y Productividad (Innóvate Perú) under the contract 452-PNICP-ECIP-2014 and the Department of Science of Pontificia Universidad Católica del Perú. The research of WL Wang was partially supported by the Ministry of Science and Technology of Taiwan (grant no. MOST 105-2118-M-035-004-MY2). The research of VH Lachos was partially supported by CNPq-Brazil (grant no. 305054/2011-2) and FAPESP-Brazil (grant no. 2014/02938-9). VI de Carvalho acknowledges support from FCT – Fundac¸ ão para a Ciência e a Tecnologia, Portugal, through the project UID/ MAT/00006/2013. CLB acknowledges support from Dirección de Gestión de la Investigación at PUCP (grant nos DGI-2014-0017/0070 and DGI-2016-1-0077).
Publisher Copyright:
© The Author(s) 2018.
PY - 2019/5/1
Y1 - 2019/5/1
N2 - 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.
AB - 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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U2 - 10.1177/0962280218760360
DO - 10.1177/0962280218760360
M3 - Article
C2 - 29551086
AN - SCOPUS:85044313656
SN - 0962-2802
VL - 28
SP - 1457
EP - 1476
JO - Statistical Methods in Medical Research
JF - Statistical Methods in Medical Research
IS - 5
ER -
