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Sample Size Determination for Logistic Regression on a Logit-Normal Distribution

机译:Logit正态分布上Logistic回归的样本量确定

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摘要

Although the sample size for simple logistic regression can be readily determined using currently available methods, the sample size calculation for multiple logistic regression requires some additional information, such as the coefficient of determination (Rcov2) of a covariate of interest with other covariates, which is often unavailable in practice. The response variable of logistic regression follows a logit-normal (LN) distribution which can be generated from a logistic transformation of a normal distribution. Using this property of logistic regression, we propose new methods of determining the sample size for simple and multiple logistic regressions using a normal transformation of outcome measures. Simulation studies and a motivating example show several advantages of the proposed methods over the existing methods: (i) no need for Rcov2 for multiple logistic regression, (ii) available interim or group-sequential designs, and (iii) much smaller required sample size.
机译:尽管可以使用当前可用的方法轻松确定用于简单logistic回归的样本量,但是用于多个logistic回归的样本量计算需要一些其他信息,例如确定系数( R cov 2 < / mn> )与其他协变量相关,在实践中通常是不可用的。 logistic回归的响应变量遵循对数正态(LN)分布,该分布可以从正态分布的logistic变换生成。利用逻辑回归的这一特性,我们提出了使用结果度量的正态转换来确定简单和多重逻辑回归的样本量的新方法。仿真研究和具有启发性的示例表明,与现有方法相比,该方法具有以下优点:(i)不需要 R cov 2 逻辑回归,(ii)可用的临时或组序设计,(iii)所需样本量小得多。

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