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Published on: September 10, 2018
Evidence synthesis for decision making 6: embedding evidence synthesis in probabilistic cost-effectiveness analysis
Sofia Dias1, Alex J Sutton2, Nicky J Welton1
1School of Social and Community Medicine, University of Bristol, Bristol, UK (SD, NJW, AEA)
Summary
Correlations between parameters in evidence synthesis can impact cost-effectiveness analyses. This paper presents four methods to properly account for joint parameter uncertainty, ensuring robust decision-making in health economics.
Area of Science:
- Health Economics
- Biostatistics
- Evidence Synthesis
Background:
- Parameter correlations arise in complex evidence synthesis models like network meta-analysis and meta-regression.
- These correlations can introduce uncertainty into incremental net benefit calculations in probabilistic decision models.
- Accurate propagation of joint parameter uncertainty, including correlations, is crucial for reliable cost-effectiveness analysis.
Purpose of the Study:
- To outline four generic approaches for evidence synthesis compatible with probabilistic cost-effectiveness analysis.
- To provide guidance on identifying situations where correlations are absent, allowing for simpler analytical methods.
- To review software facilitating data transfer and integrated analysis for flexible scenario examination.
Main Methods:
- Bayesian posterior estimation and sampling, with WinBUGS software as a popular choice for integrated analysis.
- Bayesian posterior estimation followed by exporting samples to a separate package for cost-effectiveness modeling.
- Frequentist parameter estimation with forward Monte Carlo simulation from maximum likelihood estimates and variance-covariance matrix.
- Bootstrap resampling as a frequentist simulation approach for parameter uncertainty.
Main Results:
- Four distinct, compatible methods for evidence synthesis in probabilistic cost-effectiveness analysis are detailed.
- Guidance is provided to simplify analyses when parameter correlations are negligible.
- A review of relevant software tools for data management and integrated modeling is presented.
Conclusions:
- Adopting methods that propagate joint parameter uncertainty is essential for accurate cost-effectiveness modeling.
- The presented approaches offer flexibility for researchers in handling parameter correlations.
- Appropriate software selection can enhance the efficiency and robustness of health economic evaluations.
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