Am J Epidemiol. 2021 Sep 1;190(9):1935-1947. doi: 10.1093/aje/kwab114.
Statistical correction for measurement error in epidemiologic studies is possible, provided that information about the measurement error model and its parameters are available. Such information is commonly obtained from a randomly sampled internal validation sample. It is however unknown whether randomly sampling the internal validation sample is the optimal sampling strategy. We conducted a simulation study to investigate various internal validation sampling strategies in conjunction with regression calibration. Our simulation study showed that for an internal validation study sample of 40% of the main study's sample size, stratified random and extremes sampling had a small efficiency gain over random sampling (10% and 12% decrease on average over all scenarios, respectively). The efficiency gain was more pronounced in smaller validation samples of 10% of the main study's sample size (i.e., a 31% and 36% decrease on average over all scenarios, for stratified random and extremes sampling, respectively). To mitigate the bias due to measurement error in epidemiologic studies, small efficiency gains can be achieved for internal validation sampling strategies other than random, but only when measurement error is nondifferential. For regression calibration, the gain in efficiency is, however, at the cost of a higher percentage bias and lower coverage.
在流行病学研究中，通过测量误差的统计校正，可以将医学专业学术文献翻译为简体中文。如果有关于测量误差模型及其参数的信息，则可以进行这样的校正。此类信息通常可从随机抽取的内部验证样本中获得。但是，目前尚不清楚随机抽取内部验证样本是否是最优的抽样策略。我们进行了一项模拟研究，以调查与回归校正相结合的各种内部验证抽样策略。我们的模拟研究表明，对于内部验证研究样本量为主要研究样本量的 40%的情况，分层随机抽样和极值抽样比随机抽样具有较小的效率增益（在所有情况下平均分别降低 10%和 12%）。在较小的验证样本量（即主要研究样本量的 10%）中，效率增益更为明显（对于分层随机抽样和极值抽样，在所有情况下平均分别降低 31%和 36%）。为了减轻流行病学研究中由于测量误差引起的偏差，可以通过非随机的内部验证抽样策略来获得较小的效率增益，但前提是测量误差是非差异的。对于回归校正，效率的提高是以更高的百分比偏差和更低的覆盖率为代价的。
