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归因分数在流行病学研究设计与解读中的应用。

The use of attributable fraction in the design and interpretation of epidemiologic studies.

作者信息

Adams M J, Khoury M J, James L M

机构信息

Division of Birth Defects and Developmental Disabilities, Centers for Disease Control, Atlanta, GA 30333.

出版信息

J Clin Epidemiol. 1989;42(7):659-62. doi: 10.1016/0895-4356(89)90009-7.

Abstract

Because of the etiologic heterogeneity present in many diseases and the interaction among causal factors in the development of disease, relative risks relating any one exposure to a disease may be low, especially in the presence of common exposures. Nevertheless, in the design of epidemiologic studies, arbitrary values of relative risks are often used to determine the sample size required to detect an association between a particular exposure and a disease outcome. Such an approach may not yield adequate statistical power to detect an association. In this commentary, the authors point out the value of using the attributable fraction to determine an appropriate value of relative risk to use for sample size calculations. The approach is particularly useful in cluster investigations where the magnitude of the expected attributable fraction can be readily estimated from the observed and expected rates of disease. Specification of an attributable fraction is also useful in the design of case-control studies of etiologically heterogeneous diseases, especially when common exposures are suspected. Finally, the relationship among attributable fraction, relative risk and exposure frequency is valuable in interpreting results of an epidemiologic study and gaining insight into the differences in relative risk estimates found in various studies.

摘要

由于许多疾病存在病因异质性以及疾病发展过程中因果因素之间的相互作用,将任何一种暴露与疾病关联起来的相对风险可能较低,尤其是在存在常见暴露的情况下。然而,在流行病学研究设计中,相对风险的任意值常被用于确定检测特定暴露与疾病结局之间关联所需的样本量。这种方法可能无法产生足够的统计效力来检测关联。在这篇评论中,作者指出使用归因分数来确定用于样本量计算的相对风险的适当值的价值。该方法在聚类调查中特别有用,在聚类调查中,可以根据观察到的和预期的疾病发生率轻松估计预期归因分数的大小。归因分数的设定在病因异质性疾病的病例对照研究设计中也很有用,尤其是当怀疑存在常见暴露时。最后,归因分数、相对风险和暴露频率之间的关系对于解释流行病学研究结果以及深入了解不同研究中发现的相对风险估计值差异很有价值。

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