Model uncertainty and risk estimation for experimental studies of quantal responses
Model uncertainty and risk estimation for experimental studies of quantal responses
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DOI：
10.1111/j.1539-6924.2005.00590.x
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发表时间：
2005-04-01
期刊：
影响因子：
3.8
通讯作者：
Wheeler, MW
中科院分区：
文献类型：
--
作者：
Bailer, AJ;Noble, RB;Wheeler, MW
实验动物研究经常作为预测暴露在职业危害中的人类不良反应风险的基础。将统计模型应用于暴露-反应数据，该拟合模型可用于获得与特定水平的不良反应相关的暴露估计。不幸的是，许多不同的统计模型是拟合数据的候选模型，并可能导致对风险的广泛估计。贝叶斯模型平均(BMA)提供了一种策略，用于解决在生成风险估计时选择统计模型时的不确定性。这一策略用两个例子来说明：将多阶段模型应用于癌症反应，以及第二个例子，其中不同的量化模型适用于肾脏损伤数据。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。
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.