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首页> 外文期刊>Biometrics: Journal of the Biometric Society : An International Society Devoted to the Mathematical and Statistical Aspects of Biology >A shared response model for clustered binary data in developmental toxicity studies.
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A shared response model for clustered binary data in developmental toxicity studies.

机译：在发育毒性研究中对二元数据进行聚类的共享响应模型。

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Existing distributions for modeling fetal response data in developmental toxicology such as the beta-binomial distribution have a tendency of inflating the probability of no malformed fetuses, and hence understating the risk of having at least one malformed fetus within a litter. As opposed to a shared probability extra-binomial model, we advocate a shared response model that allows a random number of fetuses within the same litter to share a common response. An explicit formula is given for the probability function and graphical plots suggest that it does not suffer from the problem of assigning too much probability to the event of no malformed fetuses. The EM algorithm can be used to estimate the model parameters. Results of a simulation study show that the EM estimates are nearly unbiased and the associated confidence intervals based on the usual standard error estimates have coverage close to the nominal level. Simulation results also suggest that the shared response model estimates of the marginalmalformation probabilities are robust to misspecification of the distributional form, but not so for the estimates of intralitter correlation and the litter-level probability of having at least one malformed fetus. The proposed model is fitted to a set of data from the U.S. National Toxicology Program. For the same dose-response relationship, the fit based on the shared response distribution is superior to that based on the beta-binomial, and comparable to that based on the recently proposed q-power distribution (Kuk, 2004, Applied Statistics53, 369-386). An advantage of the shared response model over the q-power distribution is that it is more interpretable and can be extended more easily to the multivariate case. To illustrate this, a bivariate shared response model is fitted to fetal response data involving visceral and skeletal malformation.
机译：在发育毒理学中用于对胎儿反应数据进行建模的现有分布（例如β-二项式分布）倾向于增加没有畸形胎儿的可能性，因此低估了在窝内产生至少一个畸形胎儿的风险。与共享概率二项式模型不同，我们提倡使用共享响应模型，该模型允许同一窝内随机数量的胎儿共享同一响应。为概率函数给出了一个明确的公式，图形图表明该公式不存在为没有畸形的胎儿分配过多概率的问题。 EM算法可用于估计模型参数。仿真研究的结果表明，EM估计几乎是无偏的，并且基于通常的标准误差估计的相关置信区间的覆盖率接近标称水平。模拟结果还表明，对边缘畸形概率的共享响应模型估计对于分布形式的错误指定具有较强的鲁棒性，但对于具有至少一个畸形胎儿的母体相关性和窝水平概率的估计却不那么可靠。拟议的模型适合美国国家毒理学计划的一组数据。对于相同的剂量反应关系，基于共享反应分布的拟合优于基于β二项式的拟合，并且与基于最近提出的q功率分布的拟合具有可比性（Kuk，2004; Applied Statistics53，369- 386）。共享响应模型相对于q幂分布的一个优势是，它更具解释性，并且可以更轻松地扩展到多元情况。为了说明这一点，将双变量共享反应模型拟合到涉及内脏和骨骼畸形的胎儿反应数据。

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