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Activity Number:
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380
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Type:
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Contributed
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Date/Time:
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Tuesday, August 4, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #304376 |
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Title:
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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
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Author(s):
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D. Keith Williams*+ and Zoran Bursac and Terri Wooten
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Companies:
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University of Arkansas for Medical Sciences and University of Arkansas for Medical Sciences and University of Arkansas for Medical Sciences
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Address:
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4301 W Markham Slot 781, Little Rock, AR, 72205,
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Keywords:
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Logistic regression ; power ; sample size ; SAS %PowerLog macro
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Abstract:
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Commonly when designing studies, researchers propose to measure several independent variables in a regression model, a subset of these are identified as the main variables of interest while the remainders 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 this same setting. Currently, an approach that calculates power for only one variable of interest in the presence of other covariates is in common use and works well for this special case. We propose three related algorithms and related SAS macros that extend power estimation for one or more primary variables of interest in the presence of several confounders.
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