A framework for the meta-analysis of Bland–Altman studies based on a limits of agreement approach

Elizabeth Tipton*, Jonathan Shuster

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Bland–Altman method comparison studies are common in the medical sciences and are used to compare a new measure to a gold-standard (often costlier or more invasive) measure. The distribution of these differences is summarized by two statistics, the ‘bias’ and standard deviation, and these measures are combined to provide estimates of the limits of agreement (LoA). When these LoA are within the bounds of clinically insignificant differences, the new non-invasive measure is preferred. Very often, multiple Bland–Altman studies have been conducted comparing the same two measures, and random-effects meta-analysis provides a means to pool these estimates. We provide a framework for the meta-analysis of Bland–Altman studies, including methods for estimating the LoA and measures of uncertainty (i.e., confidence intervals). Importantly, these LoA are likely to be wider than those typically reported in Bland–Altman meta-analyses. Frequently, Bland–Altman studies report results based on repeated measures designs but do not properly adjust for this design in the analysis. Meta-analyses of Bland–Altman studies frequently exclude these studies for this reason. We provide a meta-analytic approach that allows inclusion of estimates from these studies. This includes adjustments to the estimate of the standard deviation and a method for pooling the estimates based upon robust variance estimation. An example is included based on a previously published meta-analysis.

Original languageEnglish (US)
Pages (from-to)3621-3635
Number of pages15
JournalStatistics in Medicine
Volume36
Issue number23
DOIs
StatePublished - Oct 15 2017

Keywords

  • Bland–Altman method comparison study
  • meta-analysis
  • random effects
  • robust variance estimation

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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