title: Discrepancies between chained and classic utilities induced by anchoring with occasional adjustments.
abstract: BACKGROUND
  Classic utility assessment uses death and perfect health as end points. Chained utility assessment uses other health states as endpoints. It has been previously noted that these 2 assessment procedures lead to different utilities.
  
  
  OBJECTIVE
  The author attempts to explain these discrepancies between chained and classic assessments.
  
  
  METHODS
  Previous data are plotted in a uniform way to facilitate comparison. Using time trade-off and paired-comparison data, the author estimates the extent to which respondents adjust their responses when end points are varied. Data were obtained in various samples: in healthy volunteers from the general public, in students, and in women at high risk for breast cancer seeking genetic counseling.
  
  
  RESULTS
  The author obtained 741 valid data records from a total of 106 participants. The data replicate the pattern found previously. When compared to classic utilities, (1) chained utilities are smaller (larger) when the best (worst) endpoint varies and (2) the discrepancies become smaller for utilities near 0 and 1. The data reveal that there is a distinct failure to adjust responses when the end points are varied, as if the responses anchor on some master health scale. The latter finding explains the robust pattern of discrepancies.
  
  
  CONCLUSIONS
  Decision analyses that use a mix of classic and chained utilities are not on firm ground. One should be wary of normative interpretations of new value assessment procedures. Alternative interpretations of the findings are discussed.
authors.affiliation: Nijmegen Institute for Cognition and Information, The Netherlands. stalmeier@nici.kun.nl
authors.forename: Peep F M
authors.initials: PF
authors.lastname: Stalmeier
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D001943:Breast Neoplasms / Q000523:psychology / Q000601:surgery; D002755:Choice Behavior; D003258:Consumer Behavior / Q000706:statistics & numerical data*; D003362:Cost-Benefit Analysis; D003657:Decision Making; D005260:Female; D006305:Health Status Indicators; D006801:Humans; D008017:Life Expectancy; D008875:Middle Aged; D015233:Models, Statistical; D009426:Netherlands; D010342:Patient Acceptance of Health Care / Q000523:psychology*; D019057:Quality-Adjusted Life Years; D015203:Reproducibility of Results; D018570:Risk Assessment; D012494:Sampling Studies; D026712:Value of Life