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Single data extraction generated more errors than double data extraction in systematic reviews

Nina Buscemi1, Lisa Hartling, Ben Vandermeer

  • 1Department of Pediatrics, University of Alberta/Capital Health Evidence-Based Practice Centre, Edmonton, Alberta T6G 2J3, Canada. nina.buscemi@ualberta.ca

Journal of Clinical Epidemiology
|June 13, 2006
PubMed
Abstract

Insights

Single data extraction in systematic reviews leads to more errors but is faster than double data extraction. Effect estimates were similar, but increased errors warrant caution.

Area of Science:

  • Medical Informatics
  • Evidence Synthesis
  • Systematic Review Methodology

Background:

  • Systematic reviews rely on accurate data extraction.
  • The potential for errors in single vs. double data extraction is a critical concern.

Purpose of the Study:

  • To compare error frequency between single and double data extraction.
  • To evaluate differences in treatment effect estimates from both methods.
  • To assess the time efficiency of single vs. double data extraction.

Main Methods:

  • A pilot study randomized reviewers to data extraction or verification roles.
  • McNemar test compared error frequencies; paired t-test compared time.
  • Standard meta-analytic techniques calculated efficacy estimates.

Main Results:

  • Single data extraction had significantly more errors (21.7% relative difference, P=.019).
  • Effect estimates showed no substantial differences between methods for most outcomes.
  • Single data extraction was faster by 36.1% (P=.003).

Conclusions:

  • Single data extraction is associated with a higher error rate.
  • Reviewers and readers should consider potential impacts of errors on effect estimates in systematic reviews.

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