
As healthcare costs continue to rise, accurate cost-effectiveness estimates are crucial. However, estimating the costs and outcomes of different interventions is complicated by uncertainty.
In model-based health economic evaluations (HEEs), uncertainty can arise from limited data, methodological limitations, and variability in clinical outcomes.
A recent review identified 80 methods for identifying, analyzing, and communicating uncertainty in HEEs. Quantifying uncertainty wherever possible is crucial to ensure accurate and unbiased cost-effectiveness estimates, ultimately leading to optimal allocation of healthcare resources
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