title: Pilot study of a hierarchical Bayes method for utility estimation in a choice-based conjoint analysis of prescription benefit plans including medication therapy management services.
abstract: BACKGROUND
  Consumers face an array of multiattribute prescription benefit insurance programs that include different access points (retail, supermarket, Internet, etc) and levels of pharmacist interaction (including medication therapy management services [MTMSs]). Because of this, there is a need for more sophisticated information to drive prescription benefit plan design.
  
  
  OBJECTIVE
  A pilot study to determine if choice-based conjoint (CBC) analysis with hierarchical Bayes (HB) estimation for individual level part-worths could provide a stable model for attribute preferences for prescription benefit insurance; to pilot test the addition of MTMSs to a prescription benefit management model; and to pilot and compare logit-based utility estimates to HB estimations in a conjoint market simulator.
  
  
  METHODS
  A mail-based survey was conducted using a random sample of 1500 residents of the United States. A CBC analysis instrument was developed to provide a single-stated choice from a selection of different prescription benefit plans. Choice tasks were varied based on the attributes: co-payment, pharmacy access, formulary, level of pharmacist interaction including MTMSs and monthly premium. Analysis included logit-based and HB estimation for utilities, and preference share market simulation testing.
  
  
  RESULTS
  The utility estimations from HB analysis were consistent with those seen in the logit-based analysis. A goodness of fit of 83% (root likelihood) was achieved in the HB utility estimations with only 4 choice tasks per respondents and the inclusion of MTM-like services. There was convergence on preference shares from the market simulation between the 2 estimation methods.
  
  
  CONCLUSIONS
  The use of CBC analysis with HB estimation provided utilities similar to those estimated using aggregated logit-based methods, with the added benefit of respondent specific part-worth scores for each attribute level. A larger sample, changes in the instrument design, more panels (tasks) per respondent, and selection of conjoint methods may allow for more predictive information from market simulators.
authors.affiliation: Ferris State University, College of Pharmacy, 220 Ferris Drive, Big Rapids, MI 49307, USA. wellmang@ferris.edu
authors.forename: Gregory S
authors.initials: GS
authors.lastname: Wellman
authors.forename: Carla
authors.initials: C
authors.lastname: Vidican
mesh_terms: D001499:Bayes Theorem; D002755:Choice Behavior; D003625:Data Collection; D006801:Humans; D007356:Insurance, Pharmaceutical Services / Q000191:economics*; D054539:Medication Therapy Management / Q000191:economics*; D015233:Models, Statistical; D010865:Pilot Projects; D012107:Research Design; D014481:United States