Decision analysis with cumulative prospect theory.
BACKGROUND
Individuals sometimes express preferences that do not follow expected utility theory. Cumulative prospect theory adjusts for some phenomena by using decision weights rather than probabilities when analyzing a decision tree.


METHODS
The authors examined how probability transformations from cumulative prospect theory might alter a decision analysis of a prophylactic therapy in AIDS, eliciting utilities from patients with HIV infection (n = 75) and calculating expected outcomes using an established Markov model. They next focused on transformations of three sets of probabilities: 1) the probabilities used in calculating standard-gamble utility scores; 2) the probabilities of being in discrete Markov states; 3) the probabilities of transitioning between Markov states.


RESULTS
The same prophylaxis strategy yielded the highest quality-adjusted survival under all transformations. For the average patient, prophylaxis appeared relatively less advantageous when standard-gamble utilities were transformed. Prophylaxis appeared relatively more advantageous when state probabilities were transformed and relatively less advantageous when transition probabilities were transformed. Transforming standard-gamble and transition probabilities simultaneously decreased the gain from prophylaxis by almost half. Sensitivity analysis indicated that even near-linear probability weighting transformations could substantially alter quality-adjusted survival estimates.


CONCLUSIONS
The magnitude of benefit estimated in a decision-analytic model can change significantly after using cumulative prospect theory. Incorporating cumulative prospect theory into decision analysis can provide a form of sensitivity analysis and may help describe when people deviate from expected utility theory.
Bayoumi
A M
AM
Department of Medicine, University of Toronto, Ontario, Canada. ahmed.bayoumi@utoronto.ca
Redelmeier
D A
DA
D017088:AIDS-Related Opportunistic Infections / Q000188:drug therapy* / Q000401:mortality; D000163:Acquired Immunodeficiency Syndrome / Q000401:mortality / Q000517:prevention & control*; D000900:Anti-Bacterial Agents / Q000627:therapeutic use*; D017963:Azithromycin / Q000627:therapeutic use*; D003627:Data Interpretation, Statistical; D003661:Decision Support Techniques; D015658:HIV Infections / Q000188:drug therapy* / Q000401:mortality; D006801:Humans; D007407:Interviews as Topic; D008390:Markov Chains; D015270:Mycobacterium avium-intracellulare Infection / Q000188:drug therapy* / Q000401:mortality; D011336:Probability; D016019:Survival Analysis; D013997:Time Factors
D000900:Anti-Bacterial Agents; D017963:Azithromycin