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深度测量误差问题以及饮食与癌症的流行病学研究的影响力

The problem of profound mismeasurement and the power of epidemiological studies of diet and cancer.

作者信息

Freudenheim J L, Marshall J R

机构信息

Department of Social and Preventive Medicine, State University of New York, Buffalo 14214.

出版信息

Nutr Cancer. 1988;11(4):243-50. doi: 10.1080/01635588809513994.

Abstract

Ecological associations of fat intake with breast cancer incidence have not, in general, been corroborated by individual-based epidemiological studies. Profound mismeasurement, which, in these studies, probably typifies measures of dietary exposures in general and of fat in particular may, in part, explain this lack of agreement. To demonstrate the way in which error masks effects, we studied the impact of extreme mismeasurement in analysis of strong or moderate underlying associations using computer-simulated, case-control studies (300 cases, 300 controls). Severe error causes the mean and median odds ratios to be biased toward unity, tests for trend and upper category odds ratios to be often not significant, and lower category odds ratios frequently to exceed higher exposure ones. Important risk relationships can be concealed, despite careful design and analysis if there is substantial mismeasurement of exposure.

摘要

总体而言,基于个体的流行病学研究并未证实脂肪摄入量与乳腺癌发病率之间的生态关联。在这些研究中,严重的测量误差可能是导致这种不一致的部分原因,这种误差在一般饮食暴露测量中很典型,尤其是在脂肪测量方面。为了说明误差掩盖效应的方式,我们通过计算机模拟的病例对照研究(300例病例,300例对照),研究了在分析强或中等程度的潜在关联时极端测量误差的影响。严重误差会导致平均和中位数优势比偏向于1,趋势检验和高暴露组优势比往往不显著,而低暴露组优势比经常超过高暴露组。如果暴露存在大量测量误差,即使设计和分析很仔细,重要的风险关系也可能被掩盖。

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