title: Is Dimension Order Important when Valuing Health States Using Discrete Choice Experiments Including Duration?
abstract: BACKGROUND
  Discrete choice experiments with duration (DCETTO) can be used to estimate utility values for preference-based measures, such as the EQ-5D-5L. For self-completion, the health dimensions are presented in a standard order. However, for valuation, this may result in order effects. Thus, it is important to understand whether health state dimension ordering affects values. The aim of this study was to examine the importance of dimension ordering on DCE values using EQ-5D-5L.
  
  
  METHODS
  A choice experiment presenting two health profiles and a third immediate death option was developed. A three-arm study was used, with the same 120 choice sets presented online across each arm (n = 360 per arm). Arm 1 presented the standard EQ-5D-5L dimension order, arm 2 randomised order between respondents, and arm 3 randomised within respondents. Conditional logit regression was used to assess model consistency, and scale parameter testing was used to assess model poolability.
  
  
  RESULTS
  There were minor inconsistencies across each arm, but the magnitudes of the coefficients produced were generally consistent. Arm 3 produced the largest range of utility values (1 to -0.980). Scale parameter testing suggested that the models did not differ, and the data could be pooled. Follow-up questions did not suggest variation in terms of difficulty.
  
  
  CONCLUSIONS
  The results suggest that the level of randomisation used in DCE health state valuation studies does not significantly impact values, and dimension order may not be as important as other study design issues. The results support past valuation studies that use the standard order of dimensions.
authors.affiliation: University of Technology Sydney, Centre for Health Economics Research and Evaluation, 1-59 Quay St, Haymarket, Sydney, NSW, 2000, Australia. Brendan.mulhern@chere.uts.edu.au.
authors.forename: Brendan
authors.initials: B
authors.lastname: Mulhern
authors.affiliation: School of Public Health, Curtin University, Kent Street, Bentley, Perth, WA, 6102, Australia.
authors.forename: Richard
authors.initials: R
authors.lastname: Norman
authors.affiliation: Office of Health Economics, Southside, 105 Victoria Street, London, SW1E 6QT, UK.
authors.forename: Paula
authors.initials: P
authors.lastname: Lorgelly
authors.affiliation: Centre for Health Economics, Monash University, Building 75, 15 Innovation Walk, Melbourne, VIC, 3800, Australia.
authors.forename: Emily
authors.initials: E
authors.lastname: Lancsar
authors.affiliation: Flinders Health Economics Group, Flinders University Adelaide, Adelaide, SA, 5001, Australia.
authors.forename: Julie
authors.initials: J
authors.lastname: Ratcliffe
authors.affiliation: Health Economics and Decision Science, School of Health and Related Research, University of Sheffield, Regent Court, 30 Regent St, Sheffield, S1 4DA, UK.
authors.forename: John
authors.initials: J
authors.lastname: Brazier
authors.affiliation: University of Technology Sydney, Centre for Health Economics Research and Evaluation, 1-59 Quay St, Haymarket, Sydney, NSW, 2000, Australia.
authors.forename: Rosalie
authors.initials: R
authors.lastname: Viney
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D002755:Choice Behavior; D005260:Female; D006304:Health Status; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D019057:Quality-Adjusted Life Years; D011795:Surveys and Questionnaires; D055815:Young Adult