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Activity Number:
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592
- New Developments in Experiment Design and Statistical Modeling
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2018 : 2:00 PM to 3:50 PM
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Sponsor:
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International Chinese Statistical Association
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Abstract #328401
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Presentation
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Title:
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On Mean Corrected Generalized Estimating Equations
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Author(s):
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Ye Shen* and Chao Li
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Companies:
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University of Georiga and University of Georgia
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Keywords:
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GEE;
Report bias;
Longitudinal Binary Data
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Abstract:
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Generalized Estimating Equations (GEE) are often used to analyze data with repeatedly measured outcomes. However, due to the existence of report bias in self-reported outcomes, a direct application of GEE is sometimes questionable. Our motivating example from the Self-reported Cocaine use with Urine test (SCU) data was based on a study of the effect of Cognitive behavioral therapy (CBT) on cocaine dependence at the Primary Care Center of Yale-New Haven Hospital. Collected outcomes included self-reported daily drug uses and weekly urine test results. We propose Mean Corrected Generalized Estimating Equations (MCGEE) to estimate the treatment effect in self-reported binary outcomes. We demonstrate that the proposed approach yields consistent and asymptotically normally distributed estimators with unbiased contamination probability. The performance of our proposed approach is also evaluated through simulation.
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Authors who are presenting talks have a * after their name.
