Bailer A John, Noble Robert B, Wheeler Matthew W
Department of Mathematics and Statistics, Miami University, Oxford, OH 45056, USA.
Risk Anal. 2005 Apr;25(2):291-9. doi: 10.1111/j.1539-6924.2005.00590.x.
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.
实验动物研究常常作为预测接触职业危害的人类出现不良反应风险的基础。将一个统计模型应用于暴露-反应数据，这个拟合模型可用于获得与特定不良反应水平相关的暴露估计值。不幸的是，有许多不同的统计模型可供选择来拟合数据，这可能导致风险估计值差异很大。贝叶斯模型平均法（BMA）提供了一种在生成风险估计值时解决统计模型选择不确定性的策略。通过两个例子来说明这种策略：将多阶段模型应用于癌症反应，以及将不同的数量反应模型拟合到肾脏损伤数据的第二个例子。BMA提供反映模型不确定性的超额风险估计值或基准剂量估计值。
