ArticleThe international journal of biostatistics2012
Bayesian inference for a nonlinear mixed-effects Tobit model with multivariate skew-t distributions: application to AIDS studies.
Article in The international journal of biostatistics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Quantile regression-based Bayesian joint modeling analysis of longitudinal-survival data, with application to an AIDS cohort study.Lifetime data analysis · 2020Article
- Analysis of Censored Longitudinal Data with Skewness and a Terminal Event.Communications in statistics: Simulation and computation · 2017Article
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Authors and funding
2 authors.
Funding
Abstract
Censored data are characteristics of many bioassays in HIV/AIDS studies where assays may not be sensitive enough to determine gradations in viral load determination among those below a detectable threshold. Not accounting for such left-censoring appropriately can lead to biased parameter estimates in most data analysis. To properly adjust for left-censoring, this paper presents an extension of the Tobit model for fitting nonlinear dynamic mixed-effects models with skew distributions. Such extensions allow one to specify the conditional distributions for viral load response to account for left-censoring, skewness and heaviness in the tails of the distributions of the response variable. A Bayesian modeling approach via Markov Chain Monte Carlo (MCMC) algorithm is used to estimate model parameters. The proposed methods are illustrated using real data from an HIV/AIDS study.
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