MISCLASSIFICATION BIAS ARISING FROM RANDOM ERROR IN EXPOSURE MEASUREMENT - IMPLICATIONS FOR DUAL MEASUREMENT STRATEGIES
MISCLASSIFICATION BIAS ARISING FROM RANDOM ERROR IN EXPOSURE MEASUREMENT - IMPLICATIONS FOR DUAL MEASUREMENT STRATEGIES
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DOI：
10.1093/oxfordjournals.aje.a116877
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发表时间：
1993-09-15
影响因子：
5
通讯作者：
BLETTNER, M
中科院分区：
文献类型：
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作者：
BRENNER, H;BLETTNER, M
尽管会造成信息损失，但在流行病学数据分析中，经常对连续暴露变量进行分类。研究表明，在这些情况下，暴露的非微分测量误差可能导致微分错误分类。本文扩展这些发现的误分类偏见，结果从非差分测量误差，如果采用双测量策略与二分曝光。不同的情况下，随机测量误差的情况下，病例对照研究。虽然病例和对照之间的误分类率差异一般很小，但即使使用单次暴露测量，在真正的非差异暴露误分类下，对空值的偏倚通常也比预期的要弱得多。在大多数情况下，通过使用双重测量进一步降低偏差。但是，如果分析仅限于暴露分类一致的个体（两个测量值均高于或低于临界点），则需要谨慎。在这种情况下，真正的疾病关联往往被高估。
Despite the resulting loss of information, continuous exposure variables are often categorized in epidemiologic data analysis. It has been shown that nondifferential measurement error of exposure can lead to differential misclassification under these circumstances. This paper extends these findings to the misclassification bias that results from nondifferential measurement error if dual measurement strategies are employed with dichotomized exposures. Different scenarios of random measurement error are presented in the context of case-control studies. Although differences in the misclassification rates between cases and controls are generally small, the bias toward the null is usually much weaker than expected under truly nondifferential exposure misclassification even with single exposure measurements. In most cases, the bias is further reduced by the use of dual measurements. Caution is needed, however, if the analysis is restricted to individuals with concordant exposure classifications (both measurements above or both measurements below the cutpoint). In this case, the true exposure-disease association is often overestimated.