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Meta-analysis: How to quantify and explain heterogeneity?
Author(s) -
Todd Ruppar
Publication year - 2020
Publication title -
european journal of cardiovascular nursing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.914
H-Index - 50
eISSN - 1873-1953
pISSN - 1474-5151
DOI - 10.1177/1474515120944014
Subject(s) - meta analysis , medicine , study heterogeneity , random effects model , medline , systematic review , data science , management science , computer science , economics , pathology , biology , biochemistry
The number of systematic reviews and meta-analyses submitted to nursing and allied health journals continues to grow. Well-conducted and reported syntheses of research are valuable to advancing science. One of the common critiques identified in these manuscripts involves how the authors addressed heterogeneity among the studies in their meta-analyses. Methodologically inappropriate approaches regarding heterogeneity introduce error and bias into analyses and may lead to incorrect findings and conclusions. This article will discuss some of the approaches to take as well as avoid when addressing heterogeneity in meta-analyses, including suggestions for how to choose a fixed-effect or random-effects meta-analysis model and steps to follow to address heterogeneity in meta-analysis results.

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