Sample size and optimal design for logistic regression with binary interaction
Sample size and optimal design for logistic regression with binary interaction
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DOI：
10.1002/sim.2980
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发表时间：
2008-01-15
影响因子：
2
通讯作者：
Demidenko, Eugene
中科院分区：
文献类型：
--
作者：
Demidenko, Eugene
对于使用何种检验作为样本量确定和功效分析的基础，尚未达成共识。有些作者主张Wald检验，有些则主张似然比检验。我们认为，Wald测试应该使用，因为Z分数通常用于回归系数的显着性检验，因此在幂函数中应该使用相同的统计量。我们纠正了一个普遍存在的错误时，最大似然估计（MLE）的方差估计为零值的样本量确定。在我们以前的论文中，我们开发了一个正确的样本量公式，用于单次暴露的logistic回归（Statistist. 2007; 26（18）：3385-3397）。本文推导了Logistic回归中具有二元暴露和协变量的交互作用研究的封闭式公式。导出了最优控制-工况比的计算公式，使得在给定其它参数的情况下，它使幂函数最大化。我们的样本量和相互作用的功效计算可以在www.dartmouth.edu/（类似于）eugened在线进行。版权所有（c）2007约翰威利父子有限公司。
There is no consensus on what test to use as the basis for sample size determination and power analysis. Some authors advocate the Wald test and some the likelihood-ratio test. We argue that the Wald test should be used because the Z-score is commonly applied for regression coefficient significance testing and therefore the same statistic should be used in the power function. We correct a widespread mistake on sample size determination when the variance of the maximum likelihood estimate (MLE) is estimated at null value. In our previous paper, we developed a correct sample size formula for logistic regression with single exposure (Statist. Med. 2007; 26(18):3385-3397). In the present paper, closed-form formulas are derived for interaction studies with binary exposure and covariate in logistic regression. The formula for the optimal control-case ratio is derived such that it maximizes the power function given other parameters. Our sample size and power calculations with interaction can be carried out online at www.dartmouth.edu/(similar to)eugened. Copyright (c) 2007 John Wiley & Sons, Ltd.