The Added Value of Analyzing Pooled Health-Related Quality of Life Data: A Review of the EORTC PROBE Initiative
Bibliographic record
Abstract
BACKGROUND: The European Organisation for Research and Treatment of Cancer (EORTC) Patient-Reported Outcomes and Behavioural Evidence (PROBE) initiative was established to investigate critical topics to better understand health-related quality of life (HRQOL) of cancer patients and to educate clinicians, policy makers, and healthcare providers. METHODS: The aim of this paper is to review the major research outcomes of the pooled analysis of HRQOL data along with the clinical data. We identified 30 pooled EORTC randomized controlled trials (RCTs), 18 NCIC-Clinical Trials Group RCTs, and two German Ovarian Cancer Study Group RCTs, all using the EORTC QLQ-C30. All statistical tests were two-sided. RESULTS: Evidence was found that HRQOL data can offer prognostic information beyond clinical measures and improve prognostic accuracy in cancer RCTs (by 5.9%-8.3%). Moreover, models that considered both patient- and clinician-reported scores gained more prognostic overall survival accuracy for fatigue (P < .001), vomiting (P = .01), nausea (P < .001), and constipation (P = .01). Greater understanding of the association between symptom and/or functioning scales was developed by identifying physical, psychological, and gastrointestinal clusters. Additionally, minimally important differences in interpreting HRQOL changes for improvement and deterioration were found to vary across different patient populations and disease stages. Finally, HRQOL scores are statistically significantly affected by deviations from the intended time point at which the questionnaire is completed. CONCLUSIONS: The use of existing pooled data shows that it is possible to learn about general aspects of cancer HRQOL and methodology. Our work shows that setting up international pooled datasets holds great promise for understanding patients' unmet psychosocial needs and calls for additional empirical investigation to improve clinical care and understand cancer through retrospective HRQOL analyses.
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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 | 0.183 | 0.393 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.017 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".