Conducting systematic reviews of intervention questions III: Synthesizing data from intervention studies using meta-analysis

Zoonoses Public Health. 2014 Jun;61 Suppl 1:52-63. doi: 10.1111/zph.12123.


This article is the sixth in a series of six articles describing systematic reviews of interventions in animal agriculture and veterinary medicine. The first article provided an overview of systematic reviews, followed by an article on building evidence across study designs, and an article describing criteria for validity in randomized controlled trials. The fourth article in this series overviewed the initial steps in conducting a systematic review: development of a review protocol, identification of the structured question to be addressed and conducting a comprehensive literature search to identify potentially relevant research to address the review question. The fifth article introduced relevance screening of literature to identify and include research that is relevant to the review question, the use of standardized checklists and procedures to assess the risk of bias in the relevant research, data extraction from primary research studies and summarizing the results of the body of research identified. Many systematic reviews of interventions aim to use a quantitative method to combine the results of multiple studies and provide a more precise estimate of the effect of the intervention on the outcome, that is, a summary effect measure. The objective of this article was to describe general approaches that are available for quantitative synthesis of data. Specific details of all meta-analysis statistical approaches are beyond the capacity of this article.

Keywords: Systematic review; data synthesis; meta-analysis; veterinary.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Agriculture*
  • Animals
  • Confidence Intervals
  • Data Interpretation, Statistical
  • Databases, Bibliographic*
  • Meta-Analysis as Topic*
  • Reproducibility of Results
  • Research Design
  • Review Literature as Topic*
  • Selection Bias
  • Software
  • Veterinary Medicine*