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Alternative weighting schemes when performing matching-adjusted indirect comparisons.

Dan Jackson1, Kirsty Rhodes1, Mario Ouwens1

  • 1Statistical Innovation Group, AstraZeneca, Cambridge, UK.

Research Synthesis Methods
|November 1, 2020
PubMed
Summary

This study introduces an improved method for Matching-Adjusted Indirect Comparison (MAIC) to increase the effective sample size (ESS) when comparing trial data. The new approach enhances indirect treatment comparisons by maximizing data utility.

Keywords:
health technology assessmentindirect comparisonmethod of momentspopulation adjustmentpropensity score modelweight adjustment

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Area of Science:

  • Biostatistics
  • Health Economics
  • Pharmaceutical Research

Background:

  • Indirect treatment comparisons often rely on aggregate data, limiting precision.
  • Matching-Adjusted Indirect Comparison (MAIC) uses individual patient data (IPD) to adjust for covariate imbalances between trials.
  • Conventional MAIC can result in a reduced effective sample size (ESS), impacting the reliability of indirect comparisons.

Purpose of the Study:

  • To extend Matching-Adjusted Indirect Comparison (MAIC) methodology for improved population adjustment.
  • To enhance the effective sample size (ESS) in indirect treatment comparisons using individual patient data (IPD).
  • To develop a new metric for quantifying the difficulty of adjusting for covariates in MAIC.

Main Methods:

  • Employed an alternative weighting approach, building on Zubizarreta's work, to compute weights for MAIC.
  • Derived analytical results to demonstrate why the alternative approach yields a larger ESS compared to conventional MAIC.
  • Developed a new formula for maximum ESS, even with negative weights, for single covariate adjustment.

Main Results:

  • The alternative weighting method demonstrably increases the effective sample size (ESS) compared to conventional MAIC.
  • New analytical insights explain the mechanism behind the increased ESS.
  • A novel, easily computed statistic quantifies the challenge of adjusting for specific covariates.

Conclusions:

  • The proposed extension to MAIC methodology effectively enhances the ESS, leading to more robust indirect treatment comparisons.
  • The findings provide a deeper theoretical understanding and practical tools for population adjustment in research synthesis.
  • This work has significant implications for meta-analysis and network meta-analysis, improving the reliability of evidence synthesis.