BazEkon - Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie

BazEkon home page

Meny główne

Voko Zoltan (Eötvös Loránd UniversitySyreon Research Institute), Nemeth Renata (Eötvös Loránd Universit), Dank Magdolna (Semmelweis University), Nagy-Erdei Zsófia (Novartis Ltd), Kalo Zoltan (Eötvös Loránd UniversitySyreon Research Institute), Geczi Lajos (National Institute of Oncology)
Mapping the Cancer-Specific EORTC QLQ-BR23 onto the Preference-Based EuroQol-5D Instrument
Journal of Health Policy and Outcomes Research, 2013, nr 2, s. 90-99, tab., rys., bibliogr. 17 poz.
Słowa kluczowe
Technologia medyczna, Jakość życia, Choroby nowotworowe, Metody samowsporne
Medical technology, Quality of life, Cancer, Bootstrap
Background: For cost-effectiveness analysis quality of life weights estimated by preference based utility measures are needed. In many studies however, quality of life is estimated by instruments that cannot provide utility measures. The aim of the study was to derive a function which can map the EORTC QLQ-BR23 questionnaire onto the EuroQol-5D (EQ-5D) questionnaire in breast cancer patients. Methods: A cross sectional study was performed in Hungary in 615 breast cancer patients with different states of the disease. Quality of life was measured by both EORTC QLQ-BR23 and EuroQol-5D. Ordinary stepwise backward least-squares regression was used to develop a mapping function. Adjusted R2, Akaike's Information Criterion (AIC) and root mean square error (RMSE) were used to assess model performance. The robustness of the models was tested by 10-fold cross-validation and bootstrapping. Results: The "best fitting" model contained 26 BR23 item levels as predictors selected in a stepwise backward procedure. However, this model showed considerable variability in the selection of predictors. A model, which performed only marginally worse than the "best fitting model" (adjusted R2 0.44, RMSE: 0.216, AIC:-85.8) and contained the BR23 items was much more stable, therefore we considered it as the best mapping function. Conclusions: The expected value of EQ-5D can be reasonably well predicted based on the results of EORTC QLQ-BR23 in patients with breast cancer. Its applicability, however, for prediction on the individual level is limited.(original abstract)
Pełny tekst
  1. Louise Longworth L., Rowen D. NICE DSU technical support document 10: the use of mapping methods to estimate health state utility values. 2011. Available from 10 mapping FINAL.pdf; [Acessed: 06.06.2013]
  2. EuroQol Group. EuroQol-a new facility for the measurement of health-related quality of life. Health Policy 1990; 16: 199-208
  3. Sprangers MA,, Groenvold M,, Arraras JI. et al. The European Organization for Research and Treatment of Cancer breast cancer-specific quality-of-life questionnaire module: first results from a three-country field study. J Clin Oncol 1996; 14: 2756-2768
  4. The MHV Group. The measurement and valuation of health: Final report on the modeling of valuation tariffs. Centre for Health Economics, University of York; York 1995
  5. Fayers PM., Aaronson NK., Bjordal K., Groenvold M., Curran D., Bottomley A., on behalf of the EORTC Quality of Life Group. EORTC QLQ-C30 Scoring Manual (3rd edition). EORTC, Brussels 2001
  6. Krabbe PF., Peerenboom L., Langenhoff BS., Ruers TJ. Responsiveness of the generic EQ-5D summary measure compared to the disease-specific EORTC QLQ C-30. Qual Life Res 2004; 13: 1247-1253
  7. McKenzie L., Van der Pol M. Mapping the EORTC QLQ C-30 onto the EQ-5D instrument: the potential to estimate QALY's without generic preference data. Value Health 2009; 12: 167-171
  8. StataCorp. Stata Statistical Software: Release 10. StataCorp LP, College Station, TX 2007
  9. Brazier JE., Yang Y., Tsuchiya A., Rowen DL. A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures. Eur J Health Econ 2009; 11: 215-225
  10. Crott R., Briggs A. Mapping the QLQ-C30 quality of life cancer questionnaire to EQ-5D patient preferences. Eur J Health Econ 2010; 11: 427-434
  11. Kontodimopoulos N., Aletras VH., Paliouras D. Mapping the cancer-specific EORTC QLQ-C30 to the preference-based EQ- 5D, SF-6D and 15D instruments. Value Health 2009; 12: 1151-1157
  12. Kind P. Measuring the quality of life on cancer: an index based on QLQ-C30 [abstract]. J Clinical Oncol 2005; 23(16S): 6013
  13. Sullivan P., Ghushchyan V. Preference-based EQ-5D index scores for chronic conditions in the United States. Med Decis Making 2006; 26: 410-420
  14. Xie F., Pullenayegum EM., Li SC., Hopkins R., Thumboo J., Lo NN. Use of a disease-specific instrument in economic evaluations: mapping WOMAC onto the EQ-5D Utility Index. Value Health 2010; 13: 873-878
  15. Kim EJ., Ko SK., Kang HY. Mapping the cancer-specific EORTC QLQ-C30 and EORTC QLQ-BR23 to the generic EQ-5D in metastatic breast cancer patients. Qual Life Res 2012; 21: 1193-1203
  16. Lamers LM., McDonnell J., Stalmeier PF., Krabbe PF., Busschbach JJ. The Dutch tariff: results and arguments for an effective design for national EQ-5D valuation studies. Health Econ 2006; 15: 1121-1132
  17. Luo N., Johnson JA., Shaw JW., Coons SJ. A comparison of the EQ-5D index scores derived from the US and UK population-based scoring functions. Med Decis Making 2007; 27: 321-326
Cytowane przez
Udostępnij na Facebooku Udostępnij na Twitterze Udostępnij na Google+ Udostępnij na Pinterest Udostępnij na LinkedIn Wyślij znajomemu