Abstract Details
Activity Number:
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313
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Type:
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Contributed
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Date/Time:
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Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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Section for Statistical Programmers and Analysts
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Abstract - #308345 |
Title:
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Evaluating a Continuous Variable as a Proxy for Another Measure
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Author(s):
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Jonathan Mahnken*+ and Eric D. Vidoni and Sandra A. Billinger and Xueyi Chen
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Companies:
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The University of Kansas Medical Center and The University of Kansas Medical Center and The University of Kansas Medical Center and The University of Kansas Medical Center
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Keywords:
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Proxy ;
Simple linear regression ;
Testable hypotheses
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Abstract:
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To assess validity of a continuous variable (X) as a proxy measure another (Y), we used ordinary least square (OLS) regression with the proxy (X) as the sole explanatory measure for Y. A perfect proxy measure for would be unbiased (i.e., result in a regression line with a slope of one, a y-intercept of zero) with no error (mean square error equal to zero). To test the hypothesis that the slope was equal to one and the intercept zero simultaneously a contrast matrix can be used, but the resulting vector of the hypothesized equality for the contrast is not the null vector. While such hypotheses are testable manually using matrix algebra, restrictions on some software limit the ability to test such hypotheses within their regression procedures. We used a simple linear transformation to enable the simultaneous test that the slope was equal to one and the y-intercept was equal to zero using data for high-intensity (Y) and low-intensity (X) fitness measures.
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Authors who are presenting talks have a * after their name.
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