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Model-driven meta-analyses for informing health care: a diabetes meta-analysis as an exemplar
Sharon A Brown1, Betsy Jane Becker2, Alexandra A García3
1The University of Texas at Austin, TX, USA sabrown@mail.utexas.edu.
Abstract:
A relatively novel type of meta-analysis, a model-driven meta-analysis, involves the quantitative synthesis of descriptive, correlational data and is useful for identifying key predictors of health outcomes and informing clinical guidelines. Few such meta-analyses have been conducted and thus, large bodies of research remain unsynthesized and uninterpreted for application in health care. We describe the unique challenges of conducting a model-driven meta-analysis, focusing primarily on issues related to locating a sample of published and unpublished primary studies, extracting and verifying descriptive and correlational data, and conducting analyses. A current meta-analysis of the research on predictors of key health outcomes in diabetes is used to illustrate our main points.
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