NCIC Clinical Trials Group experience of employing patient-reported outcomes in clinical trials: an illustrative study in a palliative setting
Bibliographic record
Abstract
In this article we briefly review the experience of the National Cancer Institute of Canada (NCIC) Clinical Trials Group (CTG) with respect to the assessment of patient reported outcomes in clinical trials, and illustrate issues important to assessing symptom palliation in clinical trials of cancer therapy. We highlight a standard approach taken by the NCIC CTG, and illustrate how this approach may be applied to the complex problem of symptom control analysis in patients with locally advanced NSCLC. We further illustrate how variations in this analysis yield different apparent rates of palliation. Apparent rates of palliation critically depended on the outcome measures used: single symptom response across patients (5-32%, depending on the symptom of interest), symptom response in specific symptomatic patients (37-100%), symptom control (45-82%), index symptom response (60%), proportion of patients experiencing improvement in all symptoms (21%), or health-related quality of life (HRQoL) improvement (23%, global). Rates also varied substantively depending on which cohort of patients was considered relevant to each analysis (i.e., was included in the respective denominator). Substantive discordance in patients' apparent palliation was seen when HRQoL data were compared with symptom diary data. Appropriate and valid descriptions of palliative outcomes in clinical trials are complex undertakings. We conclude that several measures are required for a textured clinical description of outcome, and recommend reporting palliation according to individual symptom response rates and HRQoL response rates, in order to address each construct of palliation success.
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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 | 0.647 | 0.680 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".