Mismeasurement and the resonance of strong confounders: uncorrelated errors.

Mismeasurement and the resonance of strong confounders: uncorrelated errors.
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错误测量和强混杂因素的共振:不相关的错误。

DOI:
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发表时间:
1996
影响因子:
5
通讯作者:
J. Hastrup
J. Hastrup
中科院分区:
医学2区
文献类型:
--
作者:
J. R. Marshall;J. Hastrup

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Greenland首先证明(Am J Epidemiol 1980; 112:564-9),混杂因素测量中的错误可能引起共鸣-它可能使其他研究变量的估计值产生偏差,即使对测量的混杂因素进行统计调整,这种偏差也可能持续存在。这一发现提出了一个重要的问题:这样的偏见可以超过平凡的范围内的现实数据配置?作者研究了几种情况下,涉及二分和连续的数据,其中混杂因素和一个空变量的测量误差,他们评估的程度所产生的偏见估计的影响的空变量。他们表明,对于连续变量,测量误差达到混杂因素中观察到的方差的40%,可能导致观察到的无效研究变量的影响似乎改变了高达30%的风险。同样,他们表明,对于二分自变量,15%的错误分类形式的测量误差可能导致无效研究变量似乎改变了高达50%的风险。这种偏倚只会由强混杂引起。测量误差会掩盖强混杂是一个可能的问题的证据。这些结果支持需要每一个流行病学调查,包括在每个变量的测量误差的评价考虑。
Greenland first documented (Am J Epidemiol 1980; 112:564-9) that error in the measurement of a confounder could resonate--that it could bias estimates of other study variables, and that the bias could persist even with statistical adjustment for the confounder as measured. An important question is raised by this finding: can such bias be more than trivial within the bounds of realistic data configurations? The authors examine several situations involving dichotomous and continuous data in which a confounder and a null variable are measured with error, and they assess the extent of resultant bias in estimates of the effect of the null variable. They show that, with continuous variables, measurement error amounting to 40% of observed variance in the confounder could cause the observed impact of the null study variable to appear to alter risk by as much as 30%. Similarly, they show, with dichotomous independent variables, that 15% measurement error in the form of misclassification could lead the null study variable to appear to alter risk by as much as 50%. Such bias would result only from strong confounding. Measurement error would obscure the evidence that strong confounding is a likely problem. These results support the need for every epidemiologic inquiry to include evaluations of measurement error in each variable considered.
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