Simulation optimization of PSA-threshold based prostate cancer screening policies.

Area of Science:

• Health economics
• Biostatistics
• Medical decision making

Background:

• Prostate-specific antigen (PSA) screening guidelines impact patient outcomes and healthcare costs.
• Optimizing screening policies is crucial for maximizing benefits while minimizing harms.

Purpose of the Study:

• To develop and evaluate a simulation optimization method for designing effective PSA screening policies.
• To determine optimal screening strategies based on expected quality-adjusted life years (QALYs).

Main Methods:

• Integration of a simulation model within a genetic algorithm for policy selection.
• Utilizing a probabilistic approach for identifying the most efficient screening policy.
• Comparison of algorithm-generated policies against previously recommended screening guidelines.

Main Results:

• The developed algorithm efficiently identifies optimal PSA screening policies.
• Computational results demonstrate the algorithm's effectiveness in policy design.
• The optimized policy suggests more aggressive but shorter screening durations compared to existing recommendations.

Conclusions:

• Simulation optimization provides a robust framework for PSA screening policy design.
• Evidence supports revising current guidelines towards more intensive, shorter screening intervals.
• The proposed method can lead to improved patient outcomes, measured by QALYs.