Kim Hyang-Mi, Burstyn Igor
Department of Mathematics and Statistics, University of Calgary, Canada.
Arch Environ Occup Health. 2009 Winter;64(4):261-5. doi: 10.1080/19338240903348220.
The group-based exposure assessment has been widely used in occupational epidemiology. When the sample size used to estimate group means is "large", this leads to negligible attenuation in the estimation of odds ratio. However, the bias is proportional to the between-subject variability and is affected by the difference in true group means. We explore a Bayesian method, which adjusts in a natural way for the extra uncertainty in the outcome model associated with using the predicted values as exposures. We aim to improve the estimate obtained in naïve analysis by exploiting the properties of Berkson type error structure. We consider cases where differences in the proximity of group means and the between-subject variance are both large. The results of the simulations show that our Bayesian measurement error adjustment method that follows group-based exposure assessment improves estimates of odds ratios when the between-subject variance is large and group means are far apart.
基于群组的暴露评估已在职业流行病学中广泛应用。当用于估计群组均值的样本量“较大”时，这会导致优势比估计中的衰减可忽略不计。然而，偏差与个体间变异性成正比，并受真实群组均值差异的影响。我们探索了一种贝叶斯方法，该方法以自然的方式对与将预测值用作暴露相关的结果模型中的额外不确定性进行调整。我们旨在通过利用伯克森型误差结构的特性来改进在简单分析中获得的估计。我们考虑群组均值接近程度和个体间方差差异都很大的情况。模拟结果表明，我们基于群组暴露评估的贝叶斯测量误差调整方法在个体间方差较大且群组均值相距较远时可改善优势比的估计。
