title: Determining the impact of modeling additional sources of uncertainty in value-of-information analysis.
abstract: BACKGROUND
  The conditional reimbursement policy for expensive medicines in The Netherlands requires data collection on actual use and cost-effectiveness after the initial decision to reimburse a drug. This introduces new sources of uncertainty (less important in a randomized controlled trial than in daily practice), which may affect priorities for further research.
  
  
  OBJECTIVE
  This article focuses on determining the impact of including these uncertainties at the time a decision is made, and whether more complex models are always needed to address prioritization of additional research.
  
  
  METHODS
  We constructed a typical decision model for chronic progressive diseases with four health states and parameters related to transition and exacerbation probabilities, costs, and utilities. Different scenarios are built on the basis of three additional uncertainties: persistence, compliance, and broadening of indication. Persistence refers to treatment duration. Compliance describes the fraction of treatment benefit obtained because of not taking the medication as prescribed. Broadening of indication reflects a shift in the severity distribution at treatment start. These uncertainties were parameterized in the model and included in the value-of-information analysis.
  
  
  RESULTS
  The most important parameters were transition probabilities. Broadening of indication had little impact on the overall uncertainty. Compliance and persistence were important when establishing priorities for further research. Major differences with respect to the reference scenario were due to the parameterization of compliance in the decision model.
  
  
  CONCLUSIONS
  The usual practice of modeling only randomized controlled trial data at the time the decision on conditional reimbursement is made can lead to wrong decisions. Additional uncertainties arising from outcomes studies should be anticipated at an early stage and included in the model because this can have a strong impact on the prioritization of further research.
authors.affiliation: Institute for Medical Technology Assessment, Erasmus University Rotterdam, Rotterdam, The Netherlands. Electronic address: corroramos@imta.eur.nl.
authors.forename: Isaac Corro
authors.initials: IC
authors.lastname: Ramos
authors.affiliation: Institute for Medical Technology Assessment, Erasmus University Rotterdam, Rotterdam, The Netherlands.
authors.forename: Maureen
authors.initials: M
authors.lastname: P M H
authors.affiliation: Institute for Medical Technology Assessment, Erasmus University Rotterdam, Rotterdam, The Netherlands.
authors.forename: Rutten-van
authors.initials: RV
authors.lastname: Mölken
authors.affiliation: Institute for Medical Technology Assessment, Erasmus University Rotterdam, Rotterdam, The Netherlands.
authors.forename: Maiwenn J
authors.initials: MJ
authors.lastname: Al
mesh_terms: D003362:Cost-Benefit Analysis / Q000191:economics; D003661:Decision Support Techniques; D018450:Disease Progression; D006801:Humans; D055118:Medication Adherence; D009426:Netherlands; D035501:Uncertainty
keywords: conditional reimbursement; decision model; research prioritization; structural uncertainty; value of information