This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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237
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Type:
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Contributed
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Date/Time:
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Monday, August 2, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract - #306432 |
Title:
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Properties of Empirical Bayes Estimators for Evaluating Questionnaire Data in Epidemiology Studies
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Author(s):
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Jaya M. Satagopan*+ and Qin Zhou and Susan Oliveria and Stephen Dusza and Martin Weinstock and Marianne Berwick and Allan Halpern
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Companies:
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Memorial Sloan-Kettering Cancer Center and Memorial Sloan-Kettering Cancer Center and Memorial Sloan-Kettering Cancer Center and Memorial Sloan-Kettering Cancer Center and Brown University and University of New Mexico and Memorial Sloan-Kettering Cancer Center
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Address:
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Department of Epidemiology and Biostatistics, New York, NY, 10065,
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Keywords:
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questionnaire ;
regression ;
dimension reduction ;
empirical Bayes ;
type I error ;
bias
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Abstract:
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Cancer epidemiology studies use questionnaires to examine environmental risk factors for disease using multiple related questions. A goal of these studies is to identify the individual questions associated with disease. Regressing the outcome on the responses to individual questions may lead to counter-intuitive results due to multi-collinearity issues. An alternative is a dimension reduction analysis, where pre-defined composite summaries - i.e. linear combinations of the individual questions - are used, and effects of the individual questions are elicited in a suitable manner from this analysis. But, this approach can lead to inflated type I error and bias if the composites are defined incorrectly. To address this issue, we consider a class of empirical Bayes estimators that combines estimates from the two analyses. We investigate their properties using simulations and real data.
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