Meta-analysis: formulating, evaluating, combining, and reporting

Authors

  • Sharon-Lise T. Normand

    Corresponding author
    1. Department of Health Care Policy, Harvard Medical School, 180 Longwood Avenue, Boston, MA 02115, U.S.A., and Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, U.S.A.
    • Department of Health Care Policy, Harvard Medical School, 180 Longwood Avenue, Boston MA 02115, U.S.A.
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Abstract

Meta-analysis involves combining summary information from related but independent studies. The objectives of a meta-analysis include increasing power to detect an overall treatment effect, estimation of the degree of benefit associated with a particular study treatment, assessment of the amount of variability between studies, or identification of study characteristics associated with particularly effective treatments. This article presents a tutorial on meta-analysis intended for anyone with a mathematical statistics background. Search strategies and review methods of the literature are discussed. Emphasis is focused on analytic methods for estimation of the parameters of interest. Three modes of inference are discussed: maximum likelihood; restricted maximum likelihood, and Bayesian. Finally, software for performing inference using restricted maximum likelihood and fully Bayesian methods are demonstrated. Methods are illustrated using two examples: an evaluation of mortality from prophylactic use of lidocaine after a heart attack, and a comparison of length of hospital stay for stroke patients under two different management protocols. Copyright © 1999 John Wiley & Sons, Ltd.

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