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Mapping CushingQoL Scores onto SF-6D Utility Values in Patients with Cushing’s Syndrome

  • Montse RosetEmail author
  • Xavier Badia
  • Anna Forsythe
  • Susan M. Webb
Original Research Article

Abstract

Objectives

To construct a prediction model of preference-adjusted health status (SF-6D) for Cushing’s syndrome using a disease-specific health-related quality of life (HRQOL) measure (CushingQoL).

Methods

Data were obtained from the original multicenter, multinational study to validate the CushingQoL questionnaire. HRQOL was measured using the CushingQoL and the SF-36 questionnaires. SF-6D scores were calculated from responses on the SF-36. Sociodemographic and clinical data were also collected. Various predictive models were tested and the final one was selected on the basis of four criteria: explanatory power, consistency of estimated coefficients, normality of prediction errors, and parsimony.

Results

For the mapping analysis, data were available from 116 of the 125 patients included in the original validation study. Mean (SD) age was 45.3 (13.1) years and the sample was predominantly (83 %) female. Patients had a mean (SD) CushingQoL score of 52.9 (21.9), whereas the SF-6D (derived from SF-36) was skewed towards better health with a mean of 0.71 (median 0.74) on a scale of −0.704 to 1. Of the various models tested, a model which included the intercept (0.61), CushingQoL overall score, level one in CushingQoL item 2 (always have pain preventing me from leading a normal life), and level one in CushingQoL item 10 (my illness always affects my everyday activities) best met the four criteria for model selection. The model had an adjusted R2 of 0.60 and a root mean square error of 0.084.

Conclusions

Although the mapping function finally selected appears to be able to accurately map CushingQoL scores onto SF-6D outcomes at the group level, further testing is required to validate the model in independent patient samples.

Keywords

Root Mean Square Error Irritable Bowel Syndrome Adrenal Adenoma HRQOL Instrument Mapping Exercise 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

Montse Roset and Xavier Badia have no conflict of interest and were involved in the conception and planning of the work, statistical analysis, interpretation of the data, and the preparation of the manuscript. Anna Forsythe is a Novartis Pharmaceuticals employee. Anna Forsythe was involved in the conception and planning of the work, interpretation of the data, and the critical revision of the manuscript. Dr. Susan M. Webb received a fee for scientific and clinical assessment in the project and was involved in conception and planning of the work, interpretation of the data, and the preparation of the manuscript. Other members of the CushingQoL Development Group have no conflict of interest and were involved in interpretation of the data and the clinical review of the manuscript. All the authors approved the final submitted version of the manuscript. Montse Roset will act as overall guarantor. This study was supported by an unrestricted grant from Novartis Oncology.

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Authors and Affiliations

  • Montse Roset
    • 1
    Email author
  • Xavier Badia
    • 1
  • Anna Forsythe
    • 2
  • Susan M. Webb
    • 3
  1. 1.Health Economics and Outcomes ResearchIMS HealthBarcelonaSpain
  2. 2.Global Health Economics and Market AccessNovartis OncologyNew YorkUSA
  3. 3.Endocrinology/Medicine Departments, Hospital Sant Pau, IIB-Sant PauCentro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER Unit 747), ISCIII; Universitat Autònoma de Barcelona (UAB)BarcelonaSpain

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