Monte Carlo probabilistic sensitivity analysis for patient level simulation models: efficient estimation of mean and...

Area of Science:

• Health Economics
• Computational Statistics
• Biostatistics

Background:

• Probabilistic sensitivity analysis (PSA) is crucial for quantifying uncertainty in health economic models.
• Monte Carlo methods are commonly used for PSA but are computationally demanding for patient-level simulation models.
• The complexity of patient-level models, simulating numerous individuals, limits the feasibility of traditional PSA.

Purpose of the Study:

• To develop computationally efficient methods for PSA in patient-level simulation models.
• To reduce the computational burden associated with Monte Carlo PSA for complex health economic evaluations.
• To provide practical solutions for uncertainty analysis in micro-simulation models.

Main Methods:

• Developed novel methods based on the algebra of analysis of variance for PSA.
• Presented techniques to estimate the mean and variance of model outputs.
• Derived formulae for determining optimal sample sizes in PSA.

Main Results:

• The proposed methods substantially reduce the computational demand of Monte Carlo PSA.
• The approach enables more practical uncertainty analysis for patient-level models.
• Formulae for optimal sample size determination were provided.

Conclusions:

• The developed methods offer a significant computational advantage for PSA in patient-level models.
• This approach makes robust uncertainty analysis more feasible for complex health economic simulations.
• The findings facilitate more reliable cost-effectiveness calculations in healthcare.