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Bayesian semiparametric modeling for HIV longitudinal data with censoring and skewness.
Castro, Luis M; Wang, Wan-Lun; Lachos, Victor H; Inácio de Carvalho, Vanda; Bayes, Cristian L.
Afiliação
  • Castro LM; 1 Department of Statistics, Pontificia Universidad Católica de Chile, Chile.
  • Wang WL; 2 Department of Statistics, Graduate Institute of Statistics and Actuarial Science, Feng Chia University, Taichung, Taiwan.
  • Lachos VH; 3 Department of Statistics, University of Connecticut, Storrs, CT, USA.
  • Inácio de Carvalho V; 4 School of Mathematics, University of Edinburgh, Edinburgh, UK.
  • Bayes CL; 5 Department of Sciences, Pontificia Universidad Católica del Perú, Lima, Perú.
Stat Methods Med Res ; 28(5): 1457-1476, 2019 05.
Article em En | MEDLINE | ID: mdl-29551086
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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Infecções por HIV / Teorema de Bayes / Fármacos Anti-HIV Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Stat Methods Med Res Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Chile País de publicação: Reino Unido
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Infecções por HIV / Teorema de Bayes / Fármacos Anti-HIV Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Stat Methods Med Res Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Chile País de publicação: Reino Unido