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Uncertain about uncertainty in matching-adjusted indirect comparisons? A simulation study to compare methods for

Conor O Chandler1, Irina Proskorovsky1

  • 1Evidence Synthesis, Modeling & Communication, Evidera, Bethesda, Maryland, USA.

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PubMed
Summary

Matching-adjusted indirect comparisons (MAIC) require accurate variance estimation. Conventional estimators with effective sample size weights generally performed well, while bootstrapping showed instability in certain scenarios.

Keywords:
bootstrapmatching‐adjusted indirect comparisonrobust sandwich estimatorsimulation studyuncertaintyvariance

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Area of Science:

  • Health Technology Assessment
  • Biostatistics
  • Comparative Effectiveness Research

Background:

  • Matching-adjusted indirect comparison (MAIC) is crucial for pairwise comparisons in health technology assessment, addressing baseline characteristic imbalances.
  • Accurately quantifying uncertainty from the matching process in MAICs remains a significant challenge due to limited guidance on variance estimation.

Purpose of the Study:

  • To evaluate the performance of various statistical methods for variance estimation in MAICs.
  • To identify the most reliable methods for quantifying uncertainty in MAICs across diverse scenarios.

Main Methods:

  • A comprehensive Monte Carlo simulation study was conducted, assessing 108 scenarios.
  • Four primary variance estimation approaches were compared: conventional estimators (raw and ESS weights), sandwich estimators, and bootstrapping.
  • Performance was evaluated using coverage probabilities and variability ratios for binary and time-to-event outcomes in anchored and unanchored MAICs.

Main Results:

  • Conventional estimators with raw weights underestimated variability under poor/moderate population overlap.
  • Conventional estimators with effective sample size (ESS) weights demonstrated accurate uncertainty estimation across most scenarios.
  • Sandwich estimators showed improvement with finite sample adjustments, while bootstrapping proved unstable with poor overlap and small sample sizes.

Conclusions:

  • The choice of variance estimation method in MAICs is influenced by sample size, population overlap, and outcome type.
  • Conventional estimators using ESS weights offer a robust approach for uncertainty estimation in MAICs.
  • Further research and refined methods are needed for robust variance estimation, particularly in challenging scenarios.