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Model-Averaged Benchmark Concentration Estimates for Continuous Response Data Arising from Epidemiological Studies

机译：流行病学研究得出的连续响应数据的模型平均基准浓度估计

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摘要

Worker populations often provide data on adverse responses associated with exposure to potential hazards. The relationship between hazard exposure levels and adverse response can be modeled and then inverted to estimate the exposure associated with some specified response level. One concern is that this endpoint may be sensitive to the concentration metric and other variables included in the model. Further, it may be that the models yielding different risk endpoints are all providing relatively similar fits. We focus on evaluating the impact of exposure on a continuous response by constructing a model-averaged benchmark concentration from a weighted average of model-specific benchmark concentrations. A method for combining the estimates based on different models is applied to lung function in a cohort of miners exposed to coal dust. In this analysis, we see that a small number of the thousands of models considered survive a filtering criterion for use in averaging. Even after filtering, the models considered yield benchmark concentrations that differ by a factor of 2 to 9 depending on the concentration metric and covariates. The model-average BMC captures this uncertainty, and provides a useful strategy for addressing model uncertainty.
机译：工人人群经常提供与暴露于潜在危害相关的不良反应的数据。可以对危害暴露水平和不良反应之间的关系进行建模，然后将其反转以估计与某些特定响应水平相关的暴露。一个问题是该端点可能对浓度指标和模型中包含的其他变量敏感。此外，可能产生不同风险终点的模型都提供了相对相似的拟合。我们专注于通过从特定于模型的基准浓度的加权平均值构建模型平均基准浓度来评估暴露对连续响应的影响。将基于不同模型的估计值合并的方法应用于一群接触煤尘的矿工的肺功能。在此分析中，我们看到被考虑的成千上万的模型中有少数幸存于用于平均的过滤标准。即使经过过滤，模型仍会考虑产量基准浓度，根据浓度指标和协变量，基准浓度相差2到9倍。模型平均BMC捕获了这种不确定性，并为解决模型不确定性提供了有用的策略。

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