Mapping of QLQ-C30 and QLQ-PR25 Scores to EQ-5D-5L Utility Values for Patients with Prostate Cancer Receiving Novel...

Disease-specific questionnaires provide detailed health insights and are increasingly used in mapping studies to estimate utility values. Given the limited mapping studies in prostate cancer, our aim was to develop mapping algorithms to convert scores from disease-specific questionnaires to EQ-5D utility values for patients receiving novel androgen receptor signaling inhibitors (ARSIs).

This cross-sectional study enrolled prostate cancer patients in Taiwan. Health-related quality of life was assessed using the EQ-5D-5L, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30-item (QLQ-C30), and QLQ-Prostate Cancer 25-item (QLQ-PR25) instruments. Mapping algorithms were developed using ordinary least squares (OLS) and Tobit regression. Model performance was evaluated using the mean absolute error, root mean square error, Akaike information criterion, and Bayesian information criterion.

A total of 100 patients were included. The mean EQ-5D index score was 0.71 (standard deviation 0.41), and the mean QLQ-C30 global health status score was 70 (standard deviation 21). Patients using an ARSI for ≥2 yr tended to report lower EQ-5D-5L index scores, global health status, and physical and social functioning, along with higher levels of fatigue, pain, and hormonal treatment-related symptoms. OLS and Tobit models demonstrated good predictive performance. However, given the small sample size, the results remain exploratory and are not intended for direct clinical use.

This study provides real-world evidence on the HRQoL of Asian patients with prostate cancer on ARSI therapy. The results demonstrate that QLQ-C30 data can be effectively mapped to EQ-5D-5L utility values. These findings fill a gap in knowledge left by clinical trials.

We tested whether quality of life scores reported by patients with prostate cancer who were taking a specific type of hormone therapy could be converted to values that are used in economic analyses. The results show that our mapping method can efficiently convert scores from the QLQ-C30 patient questionnaire to economic utility scores.