Methodological Standards for Meta-Analyses and Qualitative Systematic Reviews of Cardiac Prevention and Treatment Studies: A Scientific Statement From the American Heart Association

Authors:
  • GRGoutham Rao
    Goutham Rao
  • FLFrancisco Lopez-Jimenez
    Francisco Lopez-Jimenez
  • JBJack Boyd
    Jack Boyd
  • FDFrank D’aMico
    Frank D’aMico
  • NDNefertiti H. Durant
    Nefertiti H. Durant
  • KKKatherine Kirley
    Katherine Kirley
  • CMChristopher Masi
    Christopher Masi
  • TPTiffany M. Powell-Wiley
    Tiffany M. Powell-Wiley
  • ASAnthony E. Solomonides
    Anthony E. Solomonides
  • CWColin P. West
    Colin P. West
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Abstract
Meta-analyses are becoming increasingly popular, especially in the fields of cardiovascular disease prevention and treatment. They are often considered to be a reliable source of evidence for making healthcare decisions. Unfortunately, problems among meta-analyses such as the misapplication and misinterpretation of statistical methods and tests are long-standing and widespread. The purposes of this statement are to review key steps in the development of a meta-analysis and to provide recommendations that will be useful for carrying out meta-analyses and for readers and journal editors, who must interpret the findings and gauge methodological quality. To make the statement practical and accessible, detailed descriptions of statistical methods have been omitted. Based on a survey of cardiovascular meta-analyses, published literature on methodology, expert consultation, and consensus among the writing group, key recommendations are provided. Recommendations reinforce several current practices, including protocol registration; comprehensive search strategies; methods for data extraction and abstraction; methods for identifying, measuring, and dealing with heterogeneity; and statistical methods for pooling results. Other practices should be discontinued, including the use of levels of evidence and evidence hierarchies to gauge the value and impact of different study designs (including meta-analyses) and the use of structured tools to assess the quality of studies to be included in a meta-analysis. We also recommend choosing a pooling model for conventional meta-analyses (fixed effect or random effects) on the basis of clinical and methodological similarities among studies to be included, rather than the results of a test for statistical heterogeneity.

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