Title

Mixed-effects Models with Skewed Distributions For Time-varying Decay Rate in HIV Dynamics

Document Type

Article

Publication Date

2016

Keywords

Bayesian analysis, HIV longitudinal dynamics, Mixed-effects models, Skew-elliptical distribution, Time-varying viral decay rate, 62F15, 62G05, 62G09, 62N01, 62P10

Digital Object Identifier (DOI)

https://doi.org/10.1080/03610918.2013.873129

Abstract

After initiation of treatment, HIV viral load has multiphasic changes, which indicates that the viral decay rate is a time-varying process. Mixed-effects models with different time-varying decay rate functions have been proposed in literature. However, there are two unresolved critical issues: (i) it is not clear which model is more appropriate for practical use, and (ii) the model random errors are commonly assumed to follow a normal distribution, which may be unrealistic and can obscure important features of within- and among-subject variations. Because asymmetry of HIV viral load data is still noticeable even after transformation, it is important to use a more general distribution family that enables the unrealistic normal assumption to be relaxed. We developed skew-elliptical (SE) Bayesian mixed-effects models by considering the model random errors to have an SE distribution. We compared the performance among five SE models that have different time-varying decay rate functions. For each model, we also contrasted the performance under different model random error assumptions such as normal, Student-t, skew-normal, or skew-t distribution. Two AIDS clinical trial datasets were used to illustrate the proposed models and methods. The results indicate that the model with a time-varying viral decay rate that has two exponential components is preferred. Among the four distribution assumptions, the skew-t and skew-normal models provided better fitting to the data than normal or Student-t model, suggesting that it is important to assume a model with a skewed distribution in order to achieve reasonable results when the data exhibit skewness.

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Yes

Citation / Publisher Attribution

Communications in Statistics - Simulation and Computation, v. 45, issue 2, p. 737-757

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