Odds ratios and logistic regression: further examples of their use and interpretation
Odds ratios and logistic regression: further examples of their use and interpretation
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DOI：
10.1177/1536867x0300300301
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发表时间：
2003-09-01
期刊：
影响因子：
4.8
通讯作者：
Visintainer, Paul F.
中科院分区：
文献类型：
--
作者：
Hailpern, Susan M.;Visintainer, Paul F.
Logistic回归可能是流行病学研究中最广泛使用的混杂因素校正方法。它的受欢迎程度是可以理解的。该方法可以同时调整在不同尺度上测量的混杂因素;它提供了临床可解释的估计值;并且其估计值在各种研究设计中有效，几乎没有潜在的假设。然而，对于我们这些在实践环境中的人来说，应用和解释模型的几个方面可能会令人困惑和违反直觉。我们试图通过几个例子来澄清其中的一些观点。我们应用该方法对新生儿脑室周围白质软化症和脑室内出血的危险因素进行了研究。我们将Logit模型与Cornfield的2 × 2表相关联，并讨论其在队列和病例对照研究设计中的应用。从logit模型中解释优势比、相对风险和beta（0）。
Logistic regression is perhaps the most widely used method for adjustment of confounding in epidemiologic studies. Its popularity is understandable. The method can simultaneously adjust for confounders measured on different scales; it provides estimates that are clinically interpretable; and its estimates are valid in a variety of study designs with few underlying assumptions. To those of us in practice settings, several aspects of applying and interpreting the model, however, can be confusing and counterintuitive. We attempt to clarify some of these points through several examples. We apply the method to a study of risk factors associated with periventricular leucomalacia and intraventricular hemorrhage in neonates. We relate the logit model to Cornfield's 2 x 2 table and discuss its application to both cohort and case-control study design. Interpretations of odds ratios, relative risk, and beta(0) from the logit model are presented.