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Activity Number:
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127
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Type:
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Contributed
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Date/Time:
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Monday, August 3, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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Social Statistics Section
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| Abstract - #303371 |
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Title:
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Sample Size and Power Calculations for Correlations Between Bivariate Longitudinal Data
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Author(s):
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Warren S. Comulada*+ and Robert E. Weiss
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Companies:
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University of California, Los Angeles and University of California, Los Angeles
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Address:
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Center for Community Health, 10920 Wilshire Blvd Suite 350, Los Angeles, CA, 90024,
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Keywords:
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power analysis ; bivariate longitudinal design ; repeated measures ; correlation
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Abstract:
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The analysis of a baseline predictor with a longitudinally measured outcome is well established and sample size calculations are reasonably well understood. Analyses of bivariate longitudinally measured outcomes are gaining in popularity and methods to address design issues are required. The focus in a random effects model for bivariate longitudal outcomes is on the correlations that arise between the random effects and bivariate residuals. In the random effects model, we estimate the asymptotic variances of the correlations. We propose power calculations for correlations. We compare asymptotic variance estimates to variances estimates obtained from simulation studies and compare our proposed power calculations for correlations on bivariate longitudinal data to power calculations for correlations on cross-sectional data.
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