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Meta-analysis of economic evaluation studies: data harmonisation and methodological issues.

Bhavani Shankara Bagepally1,2, Usa Chaikledkaew1,3, Nathorn Chaiyakunapruk4

  • 1Mahidol University Health Technology Assessment (MUHTA) Graduate Program, Mahidol University, Bangkok, Thailand.

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Summary

This study presents a step-by-step guide for harmonizing cost-utility analysis (CUA) data for meta-analysis. These methods enable the synthesis of economic evidence to support health policy decisions.

Keywords:
CUACost-effectivenessEconomic evaluation

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Area of Science:

  • Health Economics
  • Health Policy Research
  • Evidence Synthesis

Background:

  • Rising healthcare expenditures necessitate efficient resource allocation through economic evaluations.
  • Cost-utility analysis (CUA) is a key tool for informing health policy, but synthesizing CUA data presents methodological challenges.
  • Standardized methods are needed to prepare CUA data for meta-analysis.

Purpose of the Study:

  • To provide a systematic, step-by-step process for harmonizing cost-utility analysis (CUA) data for meta-analysis.
  • To address methodological issues in synthesizing CUA data, particularly concerning inconsistent reporting and heterogeneity.
  • To facilitate the pooling of economic evidence for robust policy recommendations.

Main Methods:

  • Developed data harmonization methods tailored to CUA, accounting for heterogeneity in economic parameters (e.g., income, time horizon, perspective, currency).
  • Estimated incremental net benefit (INB) and its variance, pooling these across studies using meta-analysis.
  • Proposed five scenarios for INB and variance estimation based on available reported data, including simulation and data borrowing techniques.

Main Results:

  • Demonstrated five distinct scenarios for calculating INB and variance from varied reported data, including mean/variance, confidence intervals, cost-effectiveness planes, and ICERs.
  • Utilized Monte Carlo simulations to estimate variance and covariance when only incremental cost and effect data were available.
  • Provided a strategy for borrowing variance INB from similar studies when direct variance data is absent.

Conclusions:

  • The proposed data harmonization and meta-analytic methods offer a practical framework for synthesizing economic evidence.
  • These methods are expected to enhance the reliability of economic evaluations, aiding policymakers in making informed decisions.
  • Facilitates the integration of diverse CUA data into a cohesive meta-analytic structure.