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Uncertainty in cost-effectiveness analysis. Probabilistic uncertainty analysis and stochastic league tables
Summary
Calculating cost-effectiveness ratios (CER) for public health interventions is challenging without individual data. This study introduces probabilistic uncertainty analysis and stochastic league tables to improve decision-making when uncertainty intervals overlap.
Area of Science:
- Health economics
- Biostatistics
- Public health policy
Background:
- Standard statistical methods for cost-effectiveness ratios (CER) are increasingly used.
- Prospective controlled trials for public health interventions are often infeasible, limiting individual-level data availability.
- Decision-making requires robust methods to handle uncertainty in intervention costs and effects.
Purpose of the Study:
- To propose and evaluate probabilistic uncertainty analysis for calculating CER.
- To guide decision-makers on interpreting overlapping uncertainty intervals in CER.
- To enhance priority setting for public health interventions using stochastic league tables.
Main Methods:
- Application of Monte Carlo simulations for probabilistic uncertainty analysis.
- Utilizing nonparametric bootstrapping techniques where appropriate.
- Developing stochastic league tables to visualize uncertainty in intervention costs and effects.
Main Results:
- Demonstrated how to incorporate uncertainty around costs and effects into league tables.
- Showcased how stochastic league tables provide additional information for decision-makers.
- Illustrated the interpretation of CER with overlapping uncertainty intervals.
Conclusions:
- Probabilistic uncertainty analysis and stochastic league tables offer valuable tools for health economic evaluation.
- These methods improve the transparency and informativeness of CER calculations, especially with limited data.
- Stochastic league tables aid decision-makers in understanding the probability of an intervention being optimal under uncertainty.
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