title: Decision analysis with cumulative prospect theory.
abstract: 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.
authors.affiliation: Department of Medicine, University of Toronto, Ontario, Canada. ahmed.bayoumi@utoronto.ca
authors.forename: A M
authors.initials: AM
authors.lastname: Bayoumi
authors.forename: D A
authors.initials: DA
authors.lastname: Redelmeier
mesh_terms: 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
chemical_list: D000900:Anti-Bacterial Agents; D017963:Azithromycin