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Related Experiment Video

Updated: Feb 17, 2026

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Quantifying heterogeneity in individual participant data meta-analysis with binary outcomes.

Bo Chen1, Andrea Benedetti2,3

  • 1Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Purvis Hall, 1020 Pine Avenue West, Montreal, Canada.

Systematic Reviews
|December 7, 2017
PubMed
Summary
This summary is machine-generated.

Quantifying heterogeneity in meta-analyses (MA) is crucial. A simulation study found that a one-stage approach for estimating I2 is superior to a two-stage approach, especially with high prevalence and effect modification.

Keywords:
HeterogeneityI 2Individual participant data meta-analysis (IPD-MA)Two-stage and one-stage approaches

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Medical Research Methodology

Background:

  • Meta-analyses (MA) often involve pooling effect estimates that exhibit substantial heterogeneity.
  • Quantifying the extent of this heterogeneity is a critical component of MA.

Purpose of the Study:

  • To evaluate methods for quantifying heterogeneity in individual participant data meta-analysis (IPD-MA) for binary data.
  • To compare the performance of one-stage and two-stage approaches in estimating heterogeneity statistics like I2 and R2.

Main Methods:

  • A simulation study was conducted to evaluate two-stage and one-stage approaches for IPD-MA of binary data.
  • Conventional I2 and R2 statistics were estimated using a two-stage approach.
  • R2 was estimated using a one-stage approach, and a simulation-based intraclass correlation coefficient (ICC) was proposed to estimate I2 from the one-stage model.

Main Results:

  • The two-stage model's estimated I2 was underestimated when no effect modification was present, while the one-stage model overestimated it.
  • In the presence of effect modification, the one-stage model's I2 estimation performed better than the two-stage model when outcome prevalence was high.
  • The two-stage model's I2 showed less sensitivity to effect modification strength with a large number of studies and low prevalence.

Conclusions:

  • A simulation-based I2 derived from a one-stage approach demonstrated superior performance compared to the conventional two-stage I2.
  • This improved performance was particularly evident under conditions of strong effect modification and high outcome prevalence.