Health-Related Quality of Life and Health Utilities in Metastatic Castrate-Resistant Prostate Cancer: A Survey Capturing Experiences from a Diverse Sample of UK Patients.

BACKGROUND

The collection of preference-based health outcomes data (or utility values) is required to support cost-effectiveness analyses.

OBJECTIVE

This study aimed to collect health-related quality of life (HRQOL) data in men with metastatic castration-resistant prostate cancer (CRPC) stratified by disease states.

METHODS

Men with metastatic CRPC were recruited via UK patient associations, patient panels, and specialist recruiters and classified into four subgroups reflecting disease state: asymptomatic/mildly symptomatic before chemotherapy, symptomatic before chemotherapy, receiving chemotherapy, and postchemotherapy. HRQOL data (including five-level EuroQol five-dimensional questionnaire [EQ-5D-5L], European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire [EORTC QLQ-C30], and 25-item prostate cancer-specific questionnaire module designed to supplement the EORTC QLQ-C30) along with background and medical history data were collected via an online survey. The EQ-5D-5L and the EORTC-8D (EORTC-8D is an 8 dimensional utility index scored from QLQ-C30 data) were both used to estimate utilities.

RESULTS

Data were collected from a total sample of 163 men with metastatic CRPC. Utility values elicited by the EQ-5D-5L ranged from 0.830 for the asymptomatic/mildly symptomatic before chemotherapy disease state (95% confidence interval [CI] 0.795-0.865) to 0.625 for the symptomatic before chemotherapy disease state (95% CI 0.577-0.673). EORTC-8D utilities ranged from 0.856 (95% CI 0.831-0.882) to 0.697 (95% CI 0.664-0.731) for the same disease/treatment states.

CONCLUSIONS

This online survey was designed to capture real-world HRQOL data describing men with CRPC. The study estimated utilities using two alternative methods, and the results show good agreement, suggesting that they are robust. This methodology offers a potentially higher quality alternative to vignette-based methods that are commonly used in oncology submissions.