Model-Driven Meta-Analyses for Informing Health Care: A Diabetes Meta-Analysis as an Exemplar

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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of conducting a model-driven meta-analysis, focusing primarily on issues related to locating a sample of published and unpublished primary studies, extracting and verifying descriptive and correlational data, and conducting analyses.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 metaanalysis of the research on predictors of key health outcomes in diabetes is used to illustrate our main points.

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