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Two-tailed asymptotic inferences for the odds ratio in prospective and retrospective studies: evaluation of methods of inference

机译:前瞻性和回顾性研究中优势比的两尾渐近推断:推断方法的评估

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Various asymptotic methods have been proposed for obtaining a two-tailed confidence interval (CI) for the odds ratio (OR) based on two independent samples. This paper evaluates 22 different methods, including 14 new methods or modifications of old methods. Because the CI is obtained by inversion in theta of the two-tailed test for H-0: OR=theta, this paper evaluates the tests for various values of theta, rather than the CIs that are obtained. The paper concludes that none of the classic methods were selected as a top method, although the Agresti logit method is an acceptable option when the sample imbalance and the true value of OR are moderate; however the CI so obtained is very wide. The best methods are based on the likelihood-ratio test and on the inverse sine transformation - which is a new method - after adding 0.5 to all the data; the likelihood-ratio test method has one disadvantage, which is that the CI is obtained iteratively. The paper also selects the best methods for ensuring compatibility between the conclusions for the CI and the independence test.
机译:已经提出了各种渐近方法,用于基于两个独立样本来获得比值比(OR)的两尾置信区间(CI)。本文评估了22种不同的方法,包括14种新方法或对旧方法的修改。由于CI是通过对H-0:OR = theta的两尾检验的theta进行反演而获得的,因此本文评估了各种theta值的测试,而不是获得的CI。本文的结论是,尽管当样品失衡和OR的真实值适中时,可以采用Agresti logit方法,但没有选择经典方法作为最佳方法。然而,如此获得的CI非常广泛。最好的方法是基于似然比检验和正弦逆变换(这是一种新方法),它是对所有数据加0.5的结果。似然比检验方法有一个缺点,就是迭代获得CI。本文还选择了最佳方法,以确保CI的结论与独立性测试之间的兼容性。

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