Health-related Quality of Life in the Phase III LUME-Colon 1 Study: Comparison and Interpretation of Results From EORTC QLQ-C30 Analyses
Bibliographic record
Abstract
INTRODUCTION: We used European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30) data from the LUME-Colon 1 study to illustrate different methods of statistical analysis for health-related quality of life (HRQoL), and compared the results. PATIENTS AND METHODS: Patients were randomized 1:1 to receive nintedanib 200 mg twice daily plus best supportive care (n = 386) or matched placebo plus best supportive care (n = 382). Five methods (mean treatment difference averaged over time, using a mixed-effects growth curve model; mixed-effects models for repeated measurements (MMRM); time-to-deterioration (TTD); status change; and responder analysis) were used to analyze EORTC QLQ-C30 global health status (GHS)/QoL and scores from functional scales. RESULTS: Overall, GHS/QoL and physical functioning deteriorated over time. Mean treatment difference slightly favored nintedanib over placebo for physical functioning (adjusted mean, 2.66; 95% confidence interval [CI], 0.97-4.34) and social functioning (adjusted mean, 2.62; 95% CI, 0.66-4.47). GHS/QoL was numerically better with nintedanib versus placebo (adjusted mean, 1.61; 95% CI, -0.004 to 3.27). MMRM analysis had similar results, with better physical functioning in the nintedanib group at all timepoints. There was no significant delay in GHS/QoL deterioration (10%) and physical functioning (16%) with nintedanib versus placebo (TTD analysis). Status change analysis showed a higher proportion of patients with markedly improved GHS/QoL and physical functioning in the nintedanib versus placebo groups. Responder analysis showed a similar, less pronounced pattern. CONCLUSION: Analyses of EORTC QLQ-C30 data showed that HRQoL was not impaired by treatment with nintedanib versus placebo. Analysis and interpretation of HRQoL endpoints should consider symptom type and severity and course of disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".