Brenner H, Loomis D
Unit of Epidemiology, University of Ulm, Germany.
Epidemiology. 1994 Sep;5(5):510-7.
Continuous exposure variables are frequently categorized in epidemiologic data analysis. It has recently been shown that such categorization may transform nondifferential error in measuring continuous exposure variables into differential exposure misclassification. This paper assesses the direction and magnitude of the resulting misclassification bias under a variety of practically relevant forms of nondifferential measurement error. The expected bias of measures of the exposure-disease association is toward the null in the case of purely random measurement error with a mean of zero. Systematic nondifferential over- or underestimation of the exposure may bias measures of the exposure-disease association either toward the null or away from the null, depending on the underlying distribution of exposure, the true exposure-disease relation, and the cutpoints employed for categorization. If exposure measurement error has both random and systematic components, the direction of the net bias is less predictable than with pure error of either type, but bias toward the null is increasingly likely as the random component grows larger. The results indicate the need for careful evaluation of potential effects of nondifferential exposure measurement error in epidemiologic studies in which categories are formed from continuous exposure variables.
在流行病学数据分析中，连续暴露变量经常被分类。最近有研究表明，这种分类可能会将测量连续暴露变量时的无差异误差转化为差异性暴露错误分类。本文评估了在各种实际相关的无差异测量误差形式下，由此产生的错误分类偏差的方向和大小。在均值为零的纯随机测量误差情况下，暴露-疾病关联测量指标的预期偏差趋向于无效值。暴露的系统性无差异高估或低估可能会使暴露-疾病关联的测量指标趋向于无效值或偏离无效值，这取决于暴露的潜在分布、真实的暴露-疾病关系以及用于分类的切点。如果暴露测量误差同时包含随机和系统成分，净偏差的方向比单一类型的纯误差更难预测，但随着随机成分增大，趋向于无效值的偏差可能性越来越大。结果表明，在由连续暴露变量形成类别进行的流行病学研究中，需要仔细评估无差异暴露测量误差的潜在影响。
