Keogh Ruth H, White Ian R
Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, U.K.
Stat Med. 2014 May 30;33(12):2137-55. doi: 10.1002/sim.6095. Epub 2014 Feb 4.
Exposure measurement error is a problem in many epidemiological studies, including those using biomarkers and measures of dietary intake. Measurement error typically results in biased estimates of exposure-disease associations, the severity and nature of the bias depending on the form of the error. To correct for the effects of measurement error, information additional to the main study data is required. Ideally, this is a validation sample in which the true exposure is observed. However, in many situations, it is not feasible to observe the true exposure, but there may be available one or more repeated exposure measurements, for example, blood pressure or dietary intake recorded at two time points. The aim of this paper is to provide a toolkit for measurement error correction using repeated measurements. We bring together methods covering classical measurement error and several departures from classical error: systematic, heteroscedastic and differential error. The correction methods considered are regression calibration, which is already widely used in the classical error setting, and moment reconstruction and multiple imputation, which are newer approaches with the ability to handle differential error. We emphasize practical application of the methods in nutritional epidemiology and other fields. We primarily consider continuous exposures in the exposure-outcome model, but we also outline methods for use when continuous exposures are categorized. The methods are illustrated using the data from a study of the association between fibre intake and colorectal cancer, where fibre intake is measured using a diet diary and repeated measures are available for a subset.
暴露测量误差在许多流行病学研究中都是一个问题，包括那些使用生物标志物和膳食摄入量测量方法的研究。测量误差通常会导致对暴露-疾病关联的估计产生偏差，偏差的严重程度和性质取决于误差的形式。为了校正测量误差的影响，需要主要研究数据之外的其他信息。理想情况下，这是一个观察到真实暴露情况的验证样本。然而，在许多情况下，观察真实暴露情况是不可行的，但可能会有一个或多个重复的暴露测量值，例如在两个时间点记录的血压或膳食摄入量。本文的目的是提供一个使用重复测量进行测量误差校正的工具包。我们汇集了涵盖经典测量误差以及与经典误差的几种偏差的方法：系统误差、异方差误差和差异误差。所考虑的校正方法有回归校准（它已在经典误差设置中广泛使用）以及矩重建和多重填补（这是能够处理差异误差的较新方法）。我们强调这些方法在营养流行病学和其他领域的实际应用。我们主要考虑暴露-结局模型中的连续暴露情况，但也概述了对连续暴露进行分类时的使用方法。使用一项关于纤维摄入量与结直肠癌关联研究的数据对这些方法进行了说明，该研究中纤维摄入量通过饮食日记进行测量，并且有一个子集的重复测量数据可用。
