The potential for a generally applicable mapping model between QLQ-C30 and SF-6D in patients with different cancers: a comparison of regression-based methods.

PURPOSE: To establish and compare generalized or "global" mapping relationships between QLQ-C30 and SF-6D, applicable across different cancer types.
METHODS: Patients (N = 671) with breast, myeloma, colorectal, lymphoma, bone marrow, prostate, lung and gastroenteric cancer were randomly split into estimation (75%) and validation (25%) datasets. SF-6D was estimated from QLQ-C30 scores via ordinary least squares, generalized linear models and median (least-absolute deviations) regression approaches, and with Bayesian additive regression kernels. Predictive ability was assessed with root mean square error, mean absolute error and proportions of predictions with absolute errors >0.05 and >0.1, whereas explanatory power with adjusted R (2) or equivalent fit measures. Two external samples (breast and colorectal cancer) were used to further test the models.
RESULTS: The QLQ-C30's global health item, the physical, emotional and social functioning scales, and the fatigue, pain and diarrhea symptom scales were significant predictors (p < 0.05 or better) in all models. Negligible deviations in models' performance were observed. All models overpredicted utilities for patients in worst health and underpredicted them for those in better health (p < 0.01 or better). Regarding external validation, performance was better in the colorectal cancer than in the breast cancer sample.
CONCLUSIONS: This study has provided evidence to support the use of "global" mapping models to predict SF-6D utilities from QLQ-C30 in patients with different cancers. Testing with diverse patient samples is required to confirm the generalizability (or not) of mapping models across cancer conditions.