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首页> 外文期刊>Source Code for Biology Medicine >Three algorithms and SAS macros for estimating power and sample size for logistic models with one or more independent variables of interest in the presence of covariates
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Three algorithms and SAS macros for estimating power and sample size for logistic models with one or more independent variables of interest in the presence of covariates

机译：三种算法和SAS宏，用于在存在协变量的情况下估计具有一个或多个相关自变量的逻辑模型的功效和样本量

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Background Commonly when designing studies, researchers propose to measure several independent variables in a regression model, a subset of which are identified as the main variables of interest while the rest are retained in a model as covariates or confounders. Power for linear regression in this setting can be calculated using SAS PROC POWER. There exists a void in estimating power for the logistic regression models in the same setting. Methods Currently, an approach that calculates power for only one variable of interest in the presence of other covariates for logistic regression is in common use and works well for this special case. In this paper we propose three related algorithms along with corresponding SAS macros that extend power estimation for one or more primary variables of interest in the presence of some confounders. Results The three proposed empirical algorithms employ likelihood ratio test to provide a user with either a power estimate for a given sample size, a quick sample size estimate for a given power, and an approximate power curve for a range of sample sizes. A user can specify odds ratios for a combination of binary, uniform and standard normal independent variables of interest, and or remaining covariates/confounders in the model, along with a correlation between variables. Conclusions These user friendly algorithms and macro tools are a promising solution that can fill the void for estimation of power for logistic regression when multiple independent variables are of interest, in the presence of additional covariates in the model.
机译：背景技术通常，在设计研究时，研究人员建议对回归模型中的几个独立变量进行测量，将其中的一个子集确定为感兴趣的主要变量，而将其余的变量作为协变量或混杂因素保留在模型中。可以使用SAS PROC POWER计算此设置中线性回归的功效。在相同设置下，逻辑回归模型的估计能力存在空白。方法目前，一种普遍存在的方法是在存在其他协变量以进行逻辑回归的情况下仅针对一个感兴趣的变量计算功效，这种方法在这种特殊情况下效果很好。在本文中，我们提出了三种相关算法以及相应的SAS宏，这些宏扩展了在存在一些混杂因素的情况下对一个或多个目标主要变量的功率估计。结果提出的三种经验算法使用似然比测试为用户提供给定样本量的功效估计，给定功效的快速样本量估算以及一系列样本量的近似功效曲线。用户可以为感兴趣的二进制，统一和标准正态自变量和/或模型中剩余的协变量/混杂因素的组合指定比值比，以及变量之间的相关性。结论这些用户友好的算法和宏工具是一种很有前途的解决方案，当在模型中存在其他协变量时，当多个自变量受到关注时，可以填补逻辑回归估计力的空白。

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