Multiple-imputation for measurement-error correction
Multiple-imputation for measurement-error correction
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DOI：
10.1093/ije/dyl097
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发表时间：
2006-08-01
影响因子：
7.7
通讯作者：
Greenland, Sander
中科院分区：
文献类型：
--
作者：
Cole, Stephen R.;Chu, Haitao;Greenland, Sander
背景测量误差修正的方法有很多。尽管测量误差无处不在，但这些方法仍然很少使用。方法将测量误差视为缺失数据问题，作者展示了如何使用SAS软件进行测量误差（MIME）校正的多重插补，并通过模拟实验评估该方法。根据完整数据，终末期肾病的估计风险比为2.0 [95%置信限（95%CL）1.4，2.8]，并降低至1.5（95% CL 1.1，2.1），使用研究入组时错误分类的低肾小球滤过率暴露（灵敏度为0.9，特异性为0.7）。MIME校正风险比为2.0（95% CL 1.2，3.3），回归校准（RC）风险比为2.0（95% CL 1.1，3.7），限制为25%验证子研究产生的风险比为2.0（95% CL 1.0，3.7）。基于蒙特卡罗模拟在八个场景，MIME是近似无偏的，有近似正确的覆盖范围，有时比错误分类或RC分析更强大。使用均方根误差作为一个标准，MIME偏差校正有时超过了增加inprecision.Conclusion MIME和RC之间的选择取决于性能，易用性和目标。MIME校正在特定应用中的有用性将取决于样本大小或验证比例。MIME校正在解释不完全测量的流行病学数据方面可能是有价值的。
Background There are many methods for measurement-error correction. These methods remain rarely used despite the ubiquity of measurement error.Methods Treating measurement error as a missing-data problem, the authors show how multiple-imputation for measurement-error (MIME) correction can be done using SAS software and evaluate the approach with a simulation experiment.Results Based on hypothetical data from a planned cohort study of 600 children with chronic kidney disease, the estimated hazard ratio for end-stage renal disease from the complete data was 2.0 [95% confidence limits (95% CL) 1.4, 2.8] and was reduced to 1.5 (95% CL 1.1, 2.1) using a misclassified exposure of low glomerular filtration rate at study entry (sensitivity of 0.9 and specificity of 0.7). The MIME correction hazard ratio was 2.0 (95% CL 1.2, 3.3), the regression calibration (RC) hazard ratio was 2.0 (95% CL 1.1, 3.7), and restriction to a 25% validation substudy yielded a hazard ratio of 2.0 (95% CL 1.0, 3.7). Based on Monte Carlo simulations across eight scenarios, MIME was approximately unbiased, had approximately correct coverage, and was sometimes more powerful than misclassified or RC analyses. Using root mean squared error as a criterion, the MIME bias correction is sometimes outweighed by added imprecision.Conclusion The choice between MIME and RC depends on performance, ease, and objectives. The usefulness of MIME correction in specific applications will depend upon the sample size or the proportion validated. MIME correction may be valuable in interpreting imperfectly measured epidemiological data.