Robbins Anthony S, Chao Susan Y, Fonseca Vincent P
Air Force Medical Operations Agency, Population Health Support Division, Brooks AFB, TX 78235-5249, USA.
Ann Epidemiol. 2002 Oct;12(7):452-4. doi: 10.1016/s1047-2797(01)00278-2.
In cohort studies of common outcomes, odds ratios (ORs) may seriously overestimate the true effect of an exposure on the outcome of interest (as measured by the risk ratio [RR]). Since few study designs require ORs (most frequently, case-control studies), their popularity is due to the widespread use of logistic regression. Because ORs are used to approximate RRs so frequently, methods have been published in the general medical literature describing how to convert ORs to RRs; however, these methods may produce inaccurate confidence intervals (CIs). The authors explore the use of binomial regression as an alternative technique to directly estimate RRs and associated CIs in cohort studies of common outcomes.
Using actual study data, the authors describe how to perform binomial regression using the SAS System for Windows, a statistical analysis program widely used by US health researchers.
In a sample data set, the OR for the exposure of interest overestimated the RR more than twofold. The 95% CIs for the OR and converted RR were wider than for the directly estimated RR.
The authors argue that for cohort studies, the use of logistic regression should be sharply curtailed, and that instead, binomial regression be used to directly estimate RRs and associated CIs.
在常见结局的队列研究中,比值比(OR)可能会严重高估暴露因素对感兴趣结局的真实效应(以风险比[RR]衡量)。由于很少有研究设计需要使用OR(最常见的是病例对照研究),其广泛应用是因为逻辑回归的广泛使用。由于OR经常被用来近似RR,普通医学文献中已发表了一些方法来描述如何将OR转换为RR;然而,这些方法可能会产生不准确的置信区间(CI)。作者探讨了使用二项式回归作为一种替代技术,在常见结局的队列研究中直接估计RR及相关CI。
作者利用实际研究数据,描述了如何使用Windows版SAS系统(美国健康研究人员广泛使用的一种统计分析程序)进行二项式回归。
在一个样本数据集中,感兴趣暴露因素的OR高估RR两倍多。OR及转换后的RR的95%CI比直接估计的RR的95%CI更宽。
作者认为,对于队列研究,应大幅减少逻辑回归的使用,而应使用二项式回归直接估计RR及相关CI。