Predicting the short form-6D preference-based index using the eight mean short form-36 health dimension scores: estimating preference-based health-related utilities when patient level data are not available

Value Health. Mar-Apr 2009;12(2):346-53. doi: 10.1111/j.1524-4733.2008.00428.x. Epub 2008 Jul 18.

Abstract

Objective: The objective is to derive an algorithm to predict a cohort preference-based short form-6D (short form-6D) score using the eight mean health dimension scores from the short form-36 (SF-36) when patient level data are not available.

Methods: Health-related quality of life data (N = 6890) covering a wide range of health conditions was used to explore the relationship between the SF-6D and the eight health dimension scores. Models obtained using ordinary least square regressions were compared for goodness of fit and predictive abilities on both within-sample subgroups and out-of-sample published data sets.

Results: The models explained more than 83% of the variance in the individual SF-6D scores with a mean absolute error of 0.040. When using mean health dimension scores from within-sample subgroups and out-of-sample published data sets, the majority of predicted scores were well within the minimal important difference (0.041) for the SF-6D.

Conclusions: This article presents a mechanism to estimate a mean cohort preference-based SF-6D score using the eight mean health dimension scores of the SF-36. Using published summary statistics, the out-of-sample validation demonstrates that the algorithms can be used to inform both clinical and economic research. Further research is required in different health conditions.

Publication types

  • Comparative Study

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Algorithms
  • Cohort Studies
  • Female
  • Forecasting
  • Health Status Indicators*
  • Health Surveys
  • Humans
  • Male
  • Middle Aged
  • Models, Statistical
  • Multivariate Analysis
  • Quality of Life*
  • Quality-Adjusted Life Years*
  • Regression Analysis*
  • Severity of Illness Index*
  • Sickness Impact Profile*
  • Statistics as Topic
  • Surveys and Questionnaires
  • Young Adult