Dagne Getachew, Huang Yangxin
University of South Florida, FL, USA.
Int J Biostat. 2012 Sep 18;8(1):/j/ijb.2012.8.issue-1/1557-4679.1387/1557-4679.1387.xml. doi: 10.1515/1557-4679.1387.
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.
删失数据是艾滋病毒/艾滋病研究中许多生物测定的特征，在这些研究中，测定可能不够灵敏，无法确定低于可检测阈值的人群中病毒载量的分级。在大多数数据分析中，若未对这种左删失进行适当考虑，可能会导致参数估计出现偏差。为了对左删失进行适当调整，本文提出了Tobit模型的扩展，用于拟合具有偏态分布的非线性动态混合效应模型。这种扩展允许人们指定病毒载量反应的条件分布，以考虑响应变量分布尾部的左删失、偏态和厚重性。通过马尔可夫链蒙特卡罗（MCMC）算法的贝叶斯建模方法用于估计模型参数。使用来自艾滋病毒/艾滋病研究的实际数据对所提出的方法进行了说明。
