JSM 2005 - Toronto

Abstract #304074

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Activity Number: 140
Type: Contributed
Date/Time: Monday, August 8, 2005 : 10:30 AM to 12:20 PM
Sponsor: General Methodology
Abstract - #304074
Title: Power Analysis for Correlations from Clustered Study Designs
Author(s): Xin Tu*+
Companies: University of Rochester
Address: Dept of Biostatistics and Comp Bio, Rochester, NY, 14642, United States
Keywords: normal distribution ; random missing data ; Pearson correlation estiamtes ; surrogacy-type assumption
Abstract:

Power analysis constitutes an important component of modern clinical trials and research studies. Although a variety of methods and software packages are available, they are primarily focused on regression models, with little attention paid to correlation analysis. However, the latter is a simpler and more appropriate approach for modeling association between correlated variables that measure a common (latent) construct using different scales, assessment methods, and raters as arising in psychosocial and other health-care related research areas. A major difficulty for performing power analysis is how to deal with the excessive number of parameters in the distributions of the correlation estimates, many of which are nuisance parameters. In addition, as missing data patterns are unpredictable and dynamic before a study is realized, its effect also must be addressed when performing power analysis, which further complicates the analytic problems. With no real data to estimate the parameters and missing data patterns as in most real-study applications, it is difficult to proceed with estimation of power and sample size for correlation analysis for a real study.

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Revised March 2005