Marshall J R, Hastrup J L
Department of Social and Preventive Medicine, State University of New York at Buffalo 14214-3000, USA.
Am J Epidemiol. 1996 May 15;143(10):1069-78. doi: 10.1093/oxfordjournals.aje.a008671.
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.
格陵兰岛最早记录（《美国流行病学杂志》1980年；112:564 - 569），混杂因素测量中的误差可能产生连锁反应——它可能使其他研究变量的估计产生偏差，并且即使对所测量的混杂因素进行统计调整，这种偏差仍可能持续存在。这一发现引发了一个重要问题：在现实的数据配置范围内，这种偏差是否会超过微不足道的程度？作者研究了几种涉及二分变量和连续变量的情况，其中混杂因素和零变量的测量存在误差，并评估了零变量效应估计中由此产生的偏差程度。他们表明，对于连续变量，混杂因素中测量误差达到观测方差的40%，可能导致零研究变量的观测影响使风险看起来改变高达30%。同样，他们表明，对于二分独立变量，15%的错误分类形式的测量误差可能导致零研究变量看起来使风险改变高达50%。这种偏差仅由强混杂因素导致。测量误差会掩盖强混杂因素可能是一个问题的证据。这些结果支持了每一项流行病学调查都需要对所考虑的每个变量的测量误差进行评估。
