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Area of Science:

  • Health Economics
  • Decision Science
  • Biostatistics

Background:

  • Probabilistic sensitivity analysis (PSA) quantifies uncertainty in cost-effectiveness analyses.
  • PSA typically involves sampling input parameters (N) and replicating stochastic models (P).
  • Determining appropriate sample sizes for N and P is critical for reliable results.

Purpose of the Study:

  • To investigate optimal methods for determining the number of parameter samples (N) and model replications (P) in PSA.
  • To ensure accurate estimation of cost-effectiveness metrics, such as the incremental cost-effectiveness ratio (ICER).
  • To provide guidance on sample size selection to avoid misleading conclusions in economic evaluations.

Main Methods:

  • Demonstrated that model replications (P) can be set arbitrarily (e.g., P=1).
  • Derived a formula based on Chebyshev's inequality to determine the required number of parameter samples (N) for a desired accuracy of the ICER.
  • Proposed visual and quantitative methods to validate the adequacy of the determined sample size N.

Main Results:

  • Arbitrarily selected sample sizes (N) can lead to significantly inaccurate ICER estimates, even with increased model replications (P).
  • The proposed Chebyshev's inequality-based method provides a data-driven approach to determine N, minimizing estimation error.
  • Validation methods confirmed that the calculated N ensures ICER estimates are within the specified accuracy levels.

Conclusions:

  • The number of parameter samples (N) in PSA should not be arbitrarily chosen; it requires a rigorous determination.
  • The study presents methods to ensure sufficient sample sizes for reliable probabilistic cost-effectiveness analyses.
  • Adhering to these methods enhances the validity and interpretability of economic evaluations.