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Abstract Details
Activity Number:
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465
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Government Statistics
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Abstract - #302968 |
Title:
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Methods of Estimation in Random Effects Meta-Regression
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Author(s):
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Abera Wouhib*+ and Myron J. Katzoff
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Companies:
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National Center for Health Statistics and Centers for Disease Control and Prevention/NCHS
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Address:
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3311 Toledo Road Rm # 3113, Hyattsville, MD, 20782,
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Keywords:
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Meta-regression model ;
Random effects ;
Heterogeneity parameter
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Abstract:
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The term meta-regression often refers to a linear models of effect size with covariates. Unlike common versions of regression analyses, which include available record-level data on outcomes and covariates, meta-regressions may consist study-level covariates in addition to record-level covariates. Random effects meta-regression models account for non-systematic differences among study outcomes which cannot be explained by sampling variability alone. We demonstrate a new estimation method using a random effect meta-regression model along with other methods of meta-analysis to estimate effect size of interest and the heterogeneity component of the variance using a simulation study. In the simulation study, we draw samples from a stochastic model that closely emulates the design of health surveys. We select the estimators of effect size which are best in terms of minimizing mean-square-error and bias. We then apply these estimators to data from health surveys and explore the causes of heterogeneity in effect sizes by including covariates at the study-level, record-level or both. Finally, we compare statistical methods, and discuss the limitations and drawbacks of these methods.
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