Decision analysis with cumulative prospect theory

Area of Science:

• Decision analysis
• Health economics
• Behavioral economics

Background:

• Expected utility theory (EUT) often fails to predict individual choices.
• Cumulative prospect theory (CPT) offers an alternative by incorporating decision weights, adjusting for psychological biases in probability assessment.

Purpose of the Study:

• To investigate the impact of CPT probability transformations on decision analysis for AIDS prophylactic therapy.
• To assess how CPT influences the estimated benefits of prophylactic interventions in HIV-infected patients.

Main Methods:

• Utilized an established Markov model to analyze prophylactic therapy in 75 HIV-infected patients.
• Applied CPT probability transformations to standard-gamble utility scores, Markov state probabilities, and transition probabilities.
• Calculated quality-adjusted survival outcomes under various CPT-informed scenarios.

Main Results:

• The optimal prophylaxis strategy remained consistent across all CPT transformations.
• CPT transformations altered the perceived advantage of prophylaxis: less advantageous with standard-gamble utility transformations, more advantageous with state probability transformations, and less advantageous with transition probability transformations.
• Simultaneous transformation of standard-gamble and transition probabilities nearly halved the estimated benefit of prophylaxis.

Conclusions:

• Decision-analytic model outcomes are sensitive to CPT probability weighting.
• Integrating CPT into decision analysis provides a valuable sensitivity analysis tool.
• CPT can help explain deviations from EUT in patient decision-making regarding health interventions.