Cancer-specific utility: clinical validation of the EORTC QLU-C10D in patients with glioblastoma
Bibliographic record
Abstract
INTRODUCTION: Many health economic evaluations rely on the validity of the utility measurement for health-related quality of life (HRQoL). While generic utility measures perform well in HRQoL assessments of many diseases and patient populations, appropriateness for cancer-specific disease burdens needs attention and condition-specific measures could be a viable option. This study assessed the clinical validity of the cancer-specific EORTC QLU-C10D, a utility scoring algorithm for the EORTC QLQ-C30, in patients with glioblastoma. We expect the EORTC QLU-C10D to be sensitive and responsive in glioblastoma patients. Furthermore, we compared its statistical efficiency with the generic utility measure EQ-5D-3L. METHODS: We used data from a multi-center randomized controlled trial (NCT00689221) with patients from 146 study sites in 25 countries. Both, the QLQ-C30 and the EQ-5D-3L, had been administered at seven assessment points together. Utilities of both measures were calculated for four country value set (Australia, Canada, UK, USA). Ceiling effects, agreement (Bland-Altman plots (BA), intra-class correlation (ICC)), were calculated to analyze construct validity. Sensitivity to known-groups (performance status; global health) and responsiveness to changes (progressive vs. non-progressive; stable vs. improved or deteriorated HRQoL) were investigated for clinical validity. Relative Efficiency (RE) was calculated to compare statistical efficiency of both utility measures. RESULTS: 435 patients were included at baseline and six subsequent time points (median timeframe 497 days). QLU-C10D country value set showed negligible ceiling effects (< 6.7%) and high agreement with EQ-5D-3L (ICC > 0.750). BA indicated that differences between both utility measures increased with deteriorating health states. While the QLU-C10D was more sensitive to global health groups (RE > 1.2), the EQ-5D-3L was more sensitive to performance status groups (RE < 0.7) than the other utility measure. Statistical efficiency to detect differences between change groups and within HRQoL deterioration group (RE > 1.4) favored QLU-C10D in 18 of 24 (75%) and 20 of 24 (83%) comparisons with the EQ-5D-3L respectively. Responsiveness to overall HRQoL change (RE > 3.4) also favored the QLU-C10D. CONCLUSION: Our results indicate that the QLU-C10D is a valid utility measure to assess HRQoL in patients with glioblastoma. This facilitates the investigation of HRQoL profiles and utilities in this patient population by administering a single questionnaire, the EORTC QLQ-C30. Efficiency analyses point to higher statistical power of the QLU-C10D compared to the EQ-5D-3L.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".