title: Estimating the value of medical treatments to patients using probabilistic multi criteria decision analysis.
abstract: BACKGROUND
  Estimating the value of medical treatments to patients is an essential part of healthcare decision making, but is mostly done implicitly and without consulting patients. Multi criteria decision analysis (MCDA) has been proposed for the valuation task, while stated preference studies are increasingly used to measure patient preferences. In this study we propose a methodology for using stated preferences to weigh clinical evidence in an MCDA model that includes uncertainty in both patient preferences and clinical evidence explicitly.
  
  
  METHODS
  A probabilistic MCDA model with an additive value function was developed and illustrated using a case on hypothetical treatments for depression. The patient-weighted values were approximated with Monte Carlo simulations and compared to expert-weighted results. Decision uncertainty was calculated as the probability of rank reversal for the first rank. Furthermore, scenario analyses were done to assess the relative impact of uncertainty in preferences and clinical evidence, and of assuming uniform preference distributions.
  
  
  RESULTS
  The patient-weighted values for drug A, drug B, drug C, and placebo were 0.51 (95% CI: 0.48 to 0.54), 0.51 (95% CI: 0.48 to 0.54), 0.54 (0.49 to 0.58), and 0.15 (95% CI: 0.13 to 0.17), respectively. Drug C was the most preferred treatment and the rank reversal probability for first rank was 27%. This probability decreased to 18% when uncertainty in performances was not included and increased to 41% when uncertainty in criterion weights was not included. With uniform preference distributions, the first rank reversal probability increased to 61%. The expert-weighted values for drug A, drug B, drug C, and placebo were 0.67 (95% CI: 0.65 to 0.68), 0.57 (95% CI: 0.56 to 0.59), 0.67 (95% CI: 0.61 to 0.71), and 0.19 (95% CI: 0.17 to 0.21). The rank reversal probability for the first rank according to experts was 49%.
  
  
  CONCLUSIONS
  Preferences elicited from patients can be used to weigh clinical evidence in a probabilistic MCDA model. The resulting treatment values can be contrasted to results from experts, and the impact of uncertainty can be quantified using rank probabilities. Future research should focus on integrating the model with regulatory decision frameworks and on including other types of uncertainty.
authors.affiliation: Department of Health Technology and Services Research, MIRA Institute, University of Twente, Enschede, The Netherlands. h.broekhuizen@utwente.nl.
authors.forename: Henk
authors.initials: H
authors.lastname: Broekhuizen
authors.affiliation: Department of Health Technology and Services Research, MIRA Institute, University of Twente, Enschede, The Netherlands. c.g.m.oudshoorn@utwente.nl.
authors.forename: Catharina G M
authors.initials: CG
authors.lastname: Groothuis-Oudshoorn
authors.affiliation: RTI Health Solutions, Research Triangle Park, NC, USA. abhauber@rti.org.
authors.forename: A Brett
authors.initials: AB
authors.lastname: Hauber
authors.affiliation: Department Public Health and Community Medicine, School of Medicine, TUFTS University, Boston, MA, USA. jeroen.jansen@tufts.edu.
authors.forename: Jeroen P
authors.initials: JP
authors.lastname: Jansen
authors.affiliation: Department of Health Technology and Services Research, MIRA Institute, University of Twente, Enschede, The Netherlands. m.j.ijzerman@utwente.nl.
authors.forename: Maarten J
authors.initials: MJ
authors.lastname: IJzerman
mesh_terms: D003661:Decision Support Techniques; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D010342:Patient Acceptance of Health Care / Q000706:statistics & numerical data*; D017060:Patient Satisfaction / Q000706:statistics & numerical data