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American Journal of Epidemiology Vol. 140, No. 3: 290-296
Copyright © 1994 by The Johns Hopkins University School of Hygiene and Public Health
research-article |
Invited Commentary: A Critical Look at Some Popular Meta-Analytic Methods
From the Department of Epidemiology, University of California at Los Angeles School of Public Health Los Angeles, CA
Correspondence to Dr Sander Greenland, 1720 Tuna Canyon Rd , Topanga, CA 90290
Meta-analysis is essential for obtaining reproducible summaries of study results and valuable for discovering patterns among study results. A good meta-analysis will highlight and delineate the subjective components of these processes and vigorously search for sources of heterogeneity. Unfortunately, these objectives are not always met by common techniques. For example, a scatterplot is an objective summarization if the data are uncensored, but inferred patterns should be regarded as subjective recognitions of the analyst, not objective data properties. Random-effects summaries encourage averaging over important data patterns, divert attention from key sources of heterogeneity, and can amplify distortions produced by publication bias; such summaries should only be used when important heterogeneity remains after a thorough search for the sources of such heterogeneity. Quality scoring adds the analyst's subjective bias to the results, wastes information, and can prevent the recognition of key sources of heterogeneity; it should be completely replaced by meta-regression on quality items (the score components). Am J Epidemiol 1994; 140:290–6.
data interpretation; statistical; epidemiologic methods; meta-analysis; models; statistical; quality scores; statistics
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