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Model Uncertainty and Risk Estimation for Experimental Studies of Quantal Responses

机译:量子响应实验研究的模型不确定性和风险估计

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摘要

Experimental animal studies often serve as the basis for predicting risk of adverse responses in humans exposed to occupational hazards. A statistical model is applied to exposure-response data and this fitted model may be used to obtain estimates of the exposure associated with a specified level of adverse response. Unfortunately, a number of different statistical models are candidates for fitting the data and may result in wide ranging estimates of risk. Bayesian model averaging (BMA) offers a strategy for addressing uncertainty in the selection of statistical models when generating risk estimates. This strategy is illustrated with two examples: applying the multistage model to cancer responses and a second example where different quantal models are fit to kidney lesion data. BMA provides excess risk estimates or benchmark dose estimates that reflects model uncertainty.
机译:动物实验研究通常作为预测暴露于职业危害的人类中不良反应风险的基础。将统计模型应用于暴露-响应数据,并且可以使用该拟合模型来获得与指定水平的不良反应相关的暴露的估计。不幸的是,许多不同的统计模型都适合拟合数据,并且可能导致广泛的风险估计。贝叶斯模型平均(BMA)提供了一种策略,用于在生成风险估计时解决统计模型选择中的不确定性。通过两个示例说明了该策略:将多阶段模型应用于癌症反应;第二个示例,其中不同的量化模型适合于肾脏病变数据。 BMA提供了反映模型不确定性的超额风险估计或基准剂量估计。

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