Kim Hyang-Mi, Yasui Yutaka, Burstyn Igor
Department of Public Health Sciences, The University of Alberta, Canada.
Ann Occup Hyg. 2006 Aug;50(6):623-35. doi: 10.1093/annhyg/mel021. Epub 2006 May 2.
In occupational epidemiology, it is often possible to obtain repeated measurements of exposure from a sample of subjects (workers) who belong to exposure groups associated with different levels of exposure. Average exposures from a sample of workers can be assigned to all members of that group including those who are not sampled, leading to a group-based exposure assessment. We discuss how this group-based exposure assessment leads to approximate Berkson error model when the number of subjects with exposure measurements in each group is large, and how the error variance approximates the between-worker variability. Under the normality assumption of exposures and with moderately large number of workers in each group, there is attenuation in the estimate of the association parameter, the magnitude of which depends on the sizes of the between-worker variability and the true association parameter. Approximate equations for attenuation have been derived in logistic and Cox proportional-hazards models. These equations show that the attenuation in Cox proportional-hazards models is generally more severe than in logistic regression. Furthermore, when the between-worker variability is large, our simulation study found that the approximation by equation is poor for the Cox proportional-hazards model. If the number of subjects is small, the approximation does not hold for either model.
在职业流行病学中，通常可以从属于与不同暴露水平相关的暴露组的受试者（工人）样本中获得重复的暴露测量值。工人样本的平均暴露量可以分配给该组的所有成员，包括那些未被采样的成员，从而形成基于组的暴露评估。我们讨论了在每组中具有暴露测量值的受试者数量较大时，这种基于组的暴露评估如何导致近似的伯克森误差模型，以及误差方差如何近似工人之间的变异性。在暴露的正态性假设下，且每组中有适度数量的工人时，关联参数的估计会出现衰减，其幅度取决于工人之间变异性的大小和真实的关联参数。在逻辑回归模型和Cox比例风险模型中已经推导出了衰减的近似方程。这些方程表明，Cox比例风险模型中的衰减通常比逻辑回归模型中更严重。此外，当工人之间的变异性较大时，我们的模拟研究发现，对于Cox比例风险模型，方程的近似效果较差。如果受试者数量较少，两种模型的近似都不成立。
