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Regression analysis with categorized regression calibrated exposure: some interesting findings

机译：带有分类回归校准暴露的回归分析：一些有趣的发现

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Background Regression calibration as a method for handling measurement error is becoming increasingly well-known and used in epidemiologic research. However, the standard version of the method is not appropriate for exposure analyzed on a categorical (e.g. quintile) scale, an approach commonly used in epidemiologic studies. A tempting solution could then be to use the predicted continuous exposure obtained through the regression calibration method and treat it as an approximation to the true exposure, that is, include the categorized calibrated exposure in the main regression analysis. Methods We use semi-analytical calculations and simulations to evaluate the performance of the proposed approach compared to the naive approach of not correcting for measurement error, in situations where analyses are performed on quintile scale and when incorporating the original scale into the categorical variables, respectively. We also present analyses of real data, containing measures of folate intake and depression, from the Norwegian Women and Cancer study (NOWAC). Results In cases where extra information is available through replicated measurements and not validation data, regression calibration does not maintain important qualities of the true exposure distribution, thus estimates of variance and percentiles can be severely biased. We show that the outlined approach maintains much, in some cases all, of the misclassification found in the observed exposure. For that reason, regression analysis with the corrected variable included on a categorical scale is still biased. In some cases the corrected estimates are analytically equal to those obtained by the naive approach. Regression calibration is however vastly superior to the naive method when applying the medians of each category in the analysis. Conclusion Regression calibration in its most well-known form is not appropriate for measurement error correction when the exposure is analyzed on a percentile scale. Relating back to the original scale of the exposure solves the problem. The conclusion regards all regression models.
机译：背景回归校准作为一种处理测量误差的方法越来越为人们所熟知，并已在流行病学研究中使用。但是，该方法的标准版本不适用于按流行病学研究常用的分类（例如五分位数）规模进行暴露分析。一个诱人的解决方案可能是使用通过回归校准方法获得的预测连续曝光并将其视为对真实曝光的近似，即在主回归分析中包括分类的校准曝光。方法在以五分位数为尺度进行分析的情况下以及将原始尺度合并到分类变量中的情况下，我们分别使用半分析计算和模拟来评估所提出的方法与不校正测量误差的简单方法相比的性能。 。我们还提供了来自挪威妇女与癌症研究（NOWAC）的真实数据分析，其中包含叶酸摄入量和抑郁症的测量值。结果如果通过重复的测量而不是验证数据可以获得更多信息，则回归校准不能保持真实暴露分布的重要质量，因此方差和百分位数的估计值可能会严重偏差。我们表明，概述的方法保留了在某些情况下观察到的暴露量发现的大部分误分类。因此，分类变量中包含已校正变量的回归分析仍然存在偏差。在某些情况下，校正后的估计值在分析上等于通过幼稚方法获得的估计值。但是，在分析中应用每个类别的中位数时，回归校准要比单纯方法好得多。结论以百分位数尺度分析曝光量时，以其最著名的形式进行回归校准不适用于测量误差校正。将其恢复为原始的曝光比例可以解决该问题。结论涉及所有回归模型。

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