Bortot Paola, Thomaseth Karl, Salvan Alberto
Department of Statistical Sciences, University of Bologna, Bologna, Italy.
Stat Med. 2002 Feb 28;21(4):533-47. doi: 10.1002/sim.999.
Understanding the kinetics of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) concentrations in humans is an important step for TCDD cancer risk assessment. In this paper longitudinal series of serum TCDD concentration measurements on U.S. veterans of the Vietnam war, who were exposed to dioxin during herbicide-spraying operations, are studied. The overall aim is to use these data to infer the dynamics of TCDD concentrations in humans. This is done by identifying a kinetic model describing the dioxin time course at the individual level. The individual toxicokinetic model is then expanded into a population model within a Bayesian hierarchical framework which allows residual variations across subjects that cannot be explained by observed covariates. Other complications in the data, such as unknown exposure histories, are also resolved implicitly through the hierarchical model. Moreover, the choice of a Bayesian approach enables the accumulation of external source of information in the form of prior distributions. The model is subjected to various diagnostic checks and analyses of sensitivity to distributional assumptions showing a good fit in terms of both the population and the kinetic features.
了解人体中2,3,7,8-四氯二苯并对二恶英（TCDD）浓度的动力学是TCDD癌症风险评估的重要一步。本文研究了在除草剂喷洒作业中接触二恶英的越南战争美国退伍军人血清TCDD浓度测量的纵向系列数据。总体目标是利用这些数据推断人体中TCDD浓度的动态变化。这是通过识别一个描述个体水平二恶英时间进程的动力学模型来实现的。然后，个体毒代动力学模型在贝叶斯分层框架内扩展为群体模型，该框架允许存在无法由观察到的协变量解释的个体间残余变异。数据中的其他复杂情况，如未知的暴露史，也通过分层模型隐含地得到解决。此外，贝叶斯方法的选择使得能够以先验分布的形式积累外部信息源。该模型经过各种诊断检查和对分布假设的敏感性分析，在群体和动力学特征方面均显示出良好的拟合度。
