Meta-analysis provides evidence-based interpretation guidelines for the clinical significance of mean differences for the FACT-G, a cancer-specific quality of life questionnaire
Bibliographic record
Abstract
Madeleine T King1, David Cella2, David Osoba3, Martin Stockler4, David Eton5, Joanna Thompson6, Amy Eisenstein71Psycho-oncology Co-operative Research Group School of Psychology, University of Sydney, New South Wales, Australia; 2Department of Medical Social Sciences, Northwestern University, Illinois, USA; 3QOL Consulting, Vancouver, British Columbia, Canada; 4NHMRC Clinical Trials Centre, University of Sydney, New South Wales, Australia; 5Mayo Clinic, Rochester, Minnesota, USA; 6Centre for Health Economics Research and Evaluation, University of Technology, Sydney, New South Wales, Australia; 7Center on Outcomes Research and Education (CORE), Evanston Northwestern Healthcare (ENH), Evanston, Illinois, USAAbstract: Our aim was to develop evidence-based interpretation guidelines for the Functional Assessment of Cancer Therapy-General (FACT-G), a cancer-specific health-related quality of life (HRQOL) instrument, from a range of clinically relevant anchors, incorporating expert judgment about clinical significance. Three clinicians with many years’ experience managing cancer patients and using HRQOL outcomes in clinical research reviewed 71 papers. Blinded to the FACT-G results, they considered the clinical anchors associated with each FACT-G mean difference, predicted which dimensions of HRQOL would be affected, and whether the effects would be trivial, small, moderate, or large. These size classes were defined in terms of clinical relevance. The experts’ judgments were then linked with FACT-G mean differences, and inverse-variance weighted mean differences were calculated for each size class. Small, medium, and large differences (95% confidence interval) from 1,118 cross-sectional comparisons were as follows: physical well-being 1.9 (0.6–3.2), 4.1 (2.7–5.5), 8.7 (5.2–12); functional well-being 2.0 (0.5–3.5), 3.8 (2.0–5.5), 8.8 (4.3–13); emotional well-being 1.0 (0.1–2.6), 1.9 (0.3–3.5), no large differences; social well-being 0.7 (-0.7 to 2.1), 0.8 (-2.9 to 4.5), no large differences. Results from 436 longitudinal comparisons tended to be smaller than the corresponding cross-sectional results. These results augment other interpretation guidelines for FACT-G with information on sample size, power calculations, and interpretation of cancer clinical trials that use FACT-G.Keywords: health-related quality of life, patient-reported outcomes
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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 | 0.410 | 0.655 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.022 | 0.049 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.011 | 0.006 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".