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Related Experiment Video

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A causal meta-analysis framework for clinical trials with unequal randomization ratios.

Dazheng Zhang1,2, Bingyu Zhang1,3, Lu Li1,3

  • 1The Center for Health AI and Synthesis of Evidence (CHASE), https://ror.org/00b30xv10University of Pennsylvania, Philadelphia, PA, USA.

Research Synthesis Methods
|March 5, 2026
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Summary
This summary is machine-generated.

This study introduces causal meta-analysis (CMA) using aggregated data for interpretable treatment effect estimation. CMA addresses limitations of standard methods, offering accurate causal effect estimates for diverse target populations.

Keywords:
causal estimandsclinical trialsmeta-analysisthe average treatment effect

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Clinical Trials

Background:

  • Meta-analysis synthesizes evidence from randomized clinical trials, informing medical practices.
  • Standard meta-analysis faces challenges like violated transportability and non-collapsible effect measures.
  • Individual participant data (IPD) is often required for causally interpretable meta-analysis.

Purpose of the Study:

  • To propose a causal meta-analysis (CMA) framework using only aggregated data.
  • To enable causally interpretable and accurate estimation of treatment effects for various target populations.
  • To address confounding bias and non-collapsibility issues in traditional meta-analysis.

Main Methods:

  • Developed a causal meta-analysis (CMA) framework utilizing aggregated data.
  • Adjusted weights for treatment effects across different target populations (ATE, ATT, ATC, ATO).
  • Mathematically derived connections between traditional meta-analysis estimators and CMA.

Main Results:

  • The proposed CMA framework enables causally interpretable treatment effect estimation without IPD.
  • CMA provides accurate estimates for diverse target populations, including ATE, ATT, ATC, and ATO.
  • Demonstrated the equivalence of Mantel-Haenszel meta-analysis to CMA with ATO.

Conclusions:

  • Causal meta-analysis (CMA) offers a robust alternative to standard meta-analysis for causal inference.
  • The CMA framework effectively handles issues of transportability and confounding bias.
  • This approach facilitates more accurate and interpretable evidence synthesis from aggregated data.