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Probabilistic sensitivity analysis (PSA) helps understand uncertainty in decision models. This guide offers a method to determine sufficient simulations for reliable cost-effectiveness analysis results, moving beyond arbitrary numbers.

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Area of Science:

  • Decision analysis
  • Health economics
  • Statistical modeling

Background:

  • Probabilistic sensitivity analysis (PSA) is crucial for assessing parameter uncertainty in decision models.
  • Current guidance on the number of simulations for PSA lacks clear definitions of 'sufficient number' or 'convergence'.
  • Existing practices often rely on arbitrary simulation counts without assessing model convergence.

Purpose of the Study:

  • To describe potential outcomes of PSA and discuss appropriate accuracy levels.
  • To provide guidance for analysts to determine when sufficient PSA simulations have been conducted for convergence.
  • To introduce a method for assessing simulation error in PSA.

Main Methods:

  • The proposed approach analyzes the variance of cost-effectiveness outcomes as a function of the number of PSA simulations.
  • A worked example using a published model demonstrates the technique.
  • The method provides a mechanism for evaluating simulation error.

Main Results:

  • The study presents a structured approach to assessing PSA convergence.
  • It offers a quantifiable method to determine an adequate number of simulations.
  • The technique allows for the evaluation of simulation error, improving upon current arbitrary practices.

Conclusions:

  • The presented technique offers a systematic way to assess PSA convergence and simulation error.
  • It provides a more rigorous approach than using arbitrary simulation numbers.
  • This method enhances the reliability of cost-effectiveness analysis by ensuring adequate model convergence.