JSM Activity #141

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Activity ID:  141
* ! Beyond the Simple Average: Adjusting for Heterogeneity and Bias in Meta-Analysis
Date / Time / Room Sponsor Type
2:00 PM - 3:50 PM
Room: H-Murray Hill Suite A
Biometrics Section*, Biopharmaceutical Section*, ENAR, Section on Health Policy Statistics* Topic Contributed
Organizer: Christopher H. Schmid, New England Medical Center
Chair: Jesse A. Berlin, University of Pennsylvania School of Medicine
Discussant: 3:25 PM - Joseph Cappelleri, Pfizer, Inc.    
Floor Discussion 3:45 PM

When studies are combined into a meta-analysis, estimation of a common study effect may be inappropriate because it is biased by the possible exclusion of unpublished studies or because it is inaccurate to describe heterogeneous effects by a single number. This session presents new methods that address these issues in the context of the limited summary data often available for analysis. The talks are aimed at the applied statistician and discuss improvements over some commonly used techniques and limitations compared with methods that are used when more extensive data are available.
  301101  By:  Russell  Localio 2:05 PM 08/12/2002
Robustness of Mixed Effects Regression to Model Misspecification in Meta Analysis

  301180  By:  Christopher H. Schmid 2:25 PM 08/12/2002
Using the Control Rate to Explore Heterogeneity of Treatment Effects in Clinical Trials

  301055  By:  Paul C. Stark 2:45 PM 08/12/2002
A Hierarchical Model for Detecting Treatment Interaction in Meta-Regression of Individual Patient and Summary Data

  301012  By:  Norma  Terrin 3:05 PM 08/12/2002
Adjusting for Publication Bias in the Presence of Heterogeneity

JSM 2002

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Revised March 2002