Simultaneous Inference for HIV Dynamic Models with Skew-<i>t</i>Distribution Incorporating Mismeasured Covariate and Multiple Treatment Factors

Huang, Yangxin; Yan, Chunning; Wu, Haipeng; Zhang, Xi

doi:10.1080/19466315.2014.916627

Cited by 1 publication

(1 citation statement)

References 28 publications

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“…Other approaches that include skewness and heavy tails for modeling HIV dynamics are the works of Huang et al (2014), who proposed a Bayesian nonlinear model for the viral load and incorporating measurement error process at the level of the CD4 cell count. The main difference between this approach and the one proposed by Bandyopadhyay et al (2015) is that the latter used the SNI class of distributions while the first one used the skew-𝑡 (ST) distribution introduced by Sahu et al (2003).…”

Section: Introductionmentioning

confidence: 99%

A Bayesian approach for mixed effects state‐space models under skewness and heavy tails

Hernandez‐Velasco,

Abanto‐Valle,

Dey

et al. 2023

Biometrical J

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Human immunodeficiency virus (HIV) dynamics have been the focus of epidemiological and biostatistical research during the past decades to understand the progression of acquired immunodeficiency syndrome (AIDS) in the population. Although there are several approaches for modeling HIV dynamics, one of the most popular is based on Gaussian mixed‐effects models because of its simplicity from the implementation and interpretation viewpoints. However, in some situations, Gaussian mixed‐effects models cannot (a) capture serial correlation existing in longitudinal data, (b) deal with missing observations properly, and (c) accommodate skewness and heavy tails frequently presented in patients' profiles. For those cases, mixed‐effects state‐space models (MESSM) become a powerful tool for modeling correlated observations, including HIV dynamics, because of their flexibility in modeling the unobserved states and the observations in a simple way. Consequently, our proposal considers an MESSM where the observations' error distribution is a skew‐t. This new approach is more flexible and can accommodate data sets exhibiting skewness and heavy tails. Under the Bayesian paradigm, an efficient Markov chain Monte Carlo algorithm is implemented. To evaluate the properties of the proposed models, we carried out some exciting simulation studies, including missing data in the generated data sets. Finally, we illustrate our approach with an application in the AIDS Clinical Trial Group Study 315 (ACTG‐315) clinical trial data set.

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Section: Introductionmentioning

confidence: 99%