Imputation for Exposure Histories with Gaps, under an Excess Relative Risk Model
Imputation for Exposure Histories with Gaps, under an Excess Relative Risk Model
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在超额相对风险模型下对有缺口的暴露历史进行插补
作者：
C. R. Weinberg;Erik S. Moledor;D. Umbach;D. Sandler
&NA;在计算累积暴露所需的重建暴露历史时，经常会出现间隙。我们的调查是出于住宅氡暴露和肺癌的病例对照研究，其中一半或更多的目标家庭可能无法测量。研究人员采用了各种方案来估算此类差距的暴露量。我们首先进行了模拟，以评估五个这样的方法下的超额相对风险模型的性能，在随机缺失的存在下，并假设独立的真实暴露水平之间的不同时期的曝光（房子）。假设没有其他测量误差来源，其中一种方法在无偏倚的情况下进行，标称95%置信区间的覆盖率接近95%。该方法将所有测量的控制住宅的算术平均值分配给缺失住宅。我们表明，它的良好性能可以解释的事实，这种方法产生近似的“Berkson错误”。为了利用可能存在的关于缺失的暴露时期的预测信息，人们可能更愿意在分层内进行估算。在进一步的模拟中，我们询问如果在许多层中进行插补，该方法是否仍然可以很好地执行。确实如此，如果分层系统能够适度预测缺失的暴露量，则可以恢复大部分损失的统计功效/精度。因此，观察到的对照平均值插补提供了一种方法来插补缺失的暴露量，而不会破坏研究的有效性;分层插补可以提高精度。该技术适用于暴露历史包含间隙的其他设置。
&NA; In reconstructing exposure histories needed to calculate cumulative exposures, gaps often occur. Our investigation was motivated by case‐control studies of residential radon exposure and lung cancer, where half or more of the targeted homes may not be measurable. Investigators have adopted various schemes for imputing exposures for such gaps. We first undertook simulations to assess the performance of five such methods under an excess relative risk model, in the presence of random missingness and under assumed independence among the true exposure levels for different epochs of exposure (houses). Assuming no other source of measurement error, one of the methods performed without bias and with coverage of nominally 95% confidence intervals that was close to 95%. This method assigns to the missing residences the arithmetic mean across all measured control residences. We show that its good properties can be explained by the fact that this approach produces approximate “Berkson errors.” To take advantage of predictive information that might exist about the missing epochs of exposure, one might prefer to carry out the imputations within strata. In further simulations, we asked whether the method would still perform well if imputations were carried out within many strata. It does, and much of the lost statistical power/precision can be recovered if the stratification system is moderately predictive of the missing exposures. Thus, observed control mean imputation provides a way to impute missing exposures without corrupting the study's validity; and stratifying the imputations can enhance precision. The technique is applicable in other settings where exposure histories contain gaps.