TY - JOUR
T1 - Mapping the cancer-specific EORTC QLQ-C30 and EORTC QLQ-BR23 to the generic EQ-5D in metastatic breast cancer patients
AU - Kim, Eun Ju
AU - Ko, Su Kyoung
AU - Kang, Hye Young
PY - 2012/9/1
Y1 - 2012/9/1
N2 - Purpose To develop a mapping algorithm for a conversion of the EORTC QLQ-C30 and EORTC QLQ BR-23 into the EQ-5D-derived utilities in metastatic breast cancer (MBC) patients. Methods We enrolled 199 patients with MBC from four leading Korean hospitals in 2009. EQ-5D utility, cancerspecific (QLQ-C30) and breast cancer-specific quality of life data (QLQ-BR23) and selected clinical and demographic information were collected from the study participants. Ordinary least squares regression models were used to model the EQ-5D using QLQ-C30 and QLQ-BR23 scale scores. To select the best model specification, six different sets of explanatory variables were compared. Result Regression analysis with the multiitem scale scores of QLQ-C30 was the best-performing model, explaining for 48.7% of the observed EQ-5D variation. Its mean absolute error between the observed and predicted EQ-5D utilities (0.092) and relative prediction error (2.784%) was among the smallest. Also, this mapping model showed the least systematic errors according to disease severity. Conclusions The mapping algorithms developed have good predictive validity, and therefore, they enable researchers to translate cancer-specific health-related quality of life measures to the preference-adjusted health status of MBC patients.
AB - Purpose To develop a mapping algorithm for a conversion of the EORTC QLQ-C30 and EORTC QLQ BR-23 into the EQ-5D-derived utilities in metastatic breast cancer (MBC) patients. Methods We enrolled 199 patients with MBC from four leading Korean hospitals in 2009. EQ-5D utility, cancerspecific (QLQ-C30) and breast cancer-specific quality of life data (QLQ-BR23) and selected clinical and demographic information were collected from the study participants. Ordinary least squares regression models were used to model the EQ-5D using QLQ-C30 and QLQ-BR23 scale scores. To select the best model specification, six different sets of explanatory variables were compared. Result Regression analysis with the multiitem scale scores of QLQ-C30 was the best-performing model, explaining for 48.7% of the observed EQ-5D variation. Its mean absolute error between the observed and predicted EQ-5D utilities (0.092) and relative prediction error (2.784%) was among the smallest. Also, this mapping model showed the least systematic errors according to disease severity. Conclusions The mapping algorithms developed have good predictive validity, and therefore, they enable researchers to translate cancer-specific health-related quality of life measures to the preference-adjusted health status of MBC patients.
UR - http://www.scopus.com/inward/record.url?scp=84867053327&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84867053327&partnerID=8YFLogxK
U2 - 10.1007/s11136-011-0037-y
DO - 10.1007/s11136-011-0037-y
M3 - Article
C2 - 22012023
AN - SCOPUS:84867053327
VL - 21
SP - 1193
EP - 1203
JO - Quality of Life Research
JF - Quality of Life Research
SN - 0962-9343
IS - 7
ER -