Mapping EORTC-QLQ-C30 onto EQ-5D-5L Index in Indonesian Cancer Patients

Document Type : Research Articles


1 Faculty of Pharmacy, Universitas Ahmad Dahlan, Yogyakarta, Indonesia.

2 Faculty of Psychology, Universitas Padjadjaran, Sumedang, Jawa Barat, Indonesia.

3 Department of Clinical Pharmacy and Community, Institute of Technology, Science and Health, Dr Soepraoen Hospital, Malang, East of Java, Indonesia.

4 Farmaza Pharmacy, Demak, Centre of Java, Indonesia.

5 Karyadi Hospital, Semarang, Indonesia.

6 Leiden University Medical Centre, Netherlands.


Objective: This study aims to develop a mapping algorithm for EORTC QLQ-C30 to EQ-5D-5L which can produce utility values in patients with cancer. Methods: We used a cross sectional study design with 300 cancer patients. The research instruments used were EORTC QLQ-C30 and EQ-5D-5L. Data were collected by interviewing cancer patients who were hospitalized in the Kasuari Installation of Dr Kariadi Hospital Semarang, Indonesia. The Ordinary Least Squares (OLS) regression method was used to predict the utility value of EQ-5D-5L. This study uses two models to predict utility values, namely model 1 with all domains, and model 2 with domains that affect the EQ-5D-5L. The predictive power of regression on the model is evaluated by calculating the mean absolute error (MAE) and root mean square error (RMSE) values. Result: The highest score in the functional domain is the ‘emotional function’ domain (mean: 85.89; SD: 16.04) and the highest symptom domain is ‘weakness’ (mean: 36.21; SD:21.69). The predicted utility values of models 1 and 2 are 0.683. The mean absolute error (MAE) and root mean square error (RMSE) values of model 1 are 0.128 and 0.173, while in model 2 the MAE and RMSE values obtained are 0.125 and 0.168. Conclusion: The development of the mapping algorithm from the EORTC QLQ-C30 to EQ-5D-5L instrument shows a predictive value of utility in a sample of patients with cancer at Dr. Kariadi Hospital, Semarang, Indonesia. The utility prediction in both model is similar, however model 2 involves fewer domains and symptoms.


Main Subjects