Allen Andrew S, Barnhart Huiman X
Department of Biostatistics and Bioinformatics and Duke Clinical Research Institute, Duke University Medical Center, Durham, NC 27715, USA.
Risk Anal. 2002 Dec;22(6):1165-73. doi: 10.1111/1539-6924.00280.
Many chemicals interfere with the natural reproductive processes in mammals. The chemicals may prevent the fertilization of an egg or keep a zygote from implanting in the uterine wall. For this reason, toxicology studies with pre-implantation exposure often exhibit a dose-related trend in the number of observed implantations per litter. Standard methods for analyzing developmental toxicology studies are conditioned on the number of implantations in the litter and therefore cannot estimate this effect of the chemical on the reproductive process. This article presents a joint modeling approach to estimating risk in toxicology studies with pre-implantation exposure. In the joint modeling approach, both the number of implanted fetuses and the outcome of each implanted fetus is modeled. Using this approach we show how to estimate the overall risk of a chemical that incorporates the risk of lost implantation due to pre-implantation exposure. Our approach has several distinct advantages over previous methods: (1) it is based on fitting a model for the observed data and, therefore, diagnostics of model fit and selection apply; (2) all assumptions are explicitly stated; and (3) it can be fit using standard software packages We illustrate our approach by analyzing a dominant lethal assay data set (Luning et al., 1966, Mutation Research, 3, 444-451) and compare ourresults with those of Rai and Van Ryzin (1985, Biometrics, 41,1-9) and Dunson (1998, Biometrics, 54, 558-569). In a simulation study, our approach has smaller bias and variance than the multiple imputation procedure of Dunson.
许多化学物质会干扰哺乳动物的自然生殖过程。这些化学物质可能会阻止卵子受精，或使受精卵无法着床于子宫壁。因此，对植入前暴露进行的毒理学研究通常显示，每窝观察到的着床数量呈现剂量相关趋势。分析发育毒理学研究的标准方法取决于窝内的着床数量，因此无法估计化学物质对生殖过程的这种影响。本文提出了一种联合建模方法，用于估计植入前暴露的毒理学研究中的风险。在联合建模方法中，对植入胎儿的数量和每个植入胎儿的结局都进行建模。使用这种方法，我们展示了如何估计一种化学物质的总体风险，该风险纳入了由于植入前暴露导致着床失败的风险。我们的方法相对于以前的方法有几个明显的优点：（1）它基于对观察数据拟合模型，因此可以应用模型拟合和选择的诊断方法；（2）所有假设都明确陈述；（3）可以使用标准软件包进行拟合。我们通过分析一个显性致死试验数据集（Luning等人，1966年，《突变研究》，3，444 - 451）来说明我们的方法，并将我们的结果与Rai和Van Ryzin（1985年，《生物统计学》，41，1 - 9）以及Dunson（1998年，《生物统计学》，54，558 - 569）的结果进行比较。在一项模拟研究中，我们的方法比Dunson的多重填补程序具有更小的偏差和方差。
