title: Thurstone scaling as a measurement method to quantify subjective health outcomes.
abstract: BACKGROUND
  Many objective health outcome measures are used to monitor patients or evaluate health interventions, but there are also subjective measures. For the latter, it is difficult to derive metric data, which are needed to quantify health outcomes such as functional disability, severity of side effects, and health status.
  
  
  OBJECTIVE
  Thurstone's Law of Comparative Judgment is presented as an alternative means to derive metric values for subjective health outcomes. The appeal of Thurstone's scaling model is that it can transform subjective individual rank order data or comparative preference data to a single group composite interval scale. To demonstrate its contribution, an empirical study was conducted, focusing on the valuation of health states.
  
  
  METHODS
  Rank order data were collected for 18 health states and were then used as input for Thurstone scaling. Visual analogue scale (VAS) values were also collected for the same states.
  
  
  METHODS
  An agency for market research recruited 212 Dutch respondents aged 18-75 years.
  
  
  RESULTS
  The derived Thurstone values showed a strong relationship with the VAS values. The positions of the 2 worst states were almost identical on the VAS and the Thurstone scale. Intermediate states were scaled somewhat differently by the 2 methods.
  
  
  CONCLUSIONS
  For many subjective health outcomes, Thurstone scaling and its derivatives may be an attractive methodology to arrive at quantitative measures.
authors: Department of Epidemiology, Biostatistics, and Health Technology Assessment, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands. p.krabbe@mta.umcn.nl
authors: Paul F M
authors: PF
authors: Krabbe
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D003657:Decision Making; D005260:Female; D006304:Health Status; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D036301:Qualitative Research; D015203:Reproducibility of Results; D012107:Research Design; D012720:Severity of Illness Index