Quality of life as a prognostic indicator of survival: A pooled analysis of individual patient data from canadian cancer trials group clinical trials
Bibliographic record
Abstract
BACKGROUND: The aims of this study were to externally validate an established association between baseline health-related quality of life (HRQOL) scores and survival and to assess the added prognostic value of HRQOL with respect to demographic and clinical indicators. METHODS: Pooled data were analyzed from 17 randomized controlled trials opened by the Canadian Cancer Trials Group between 1991 and 2004; they included survival and baseline HRQOL data from 3606 patients with 8 different cancer sites. The models included sex, age (≤60 vs >60 years), World Health Organization performance status (0 or 1 vs 2-4), distant metastases (no vs yes), and 15 European Organization for Research and Treatment of Cancer (EORTC) Core Quality-of-Life Questionnaire (QLQ-C30) scales. Analyses were conducted with multivariate Cox proportional hazards models and were stratified by cancer site. Harrell's discrimination C-index was used to calculate the predictive accuracy of the model when HRQOL parameters were added to clinical and demographic variables. The added value of adding HRQOL scales to clinical and demographic variables was illustrated with Kaplan-Meier curves. RESULTS: In the stratified, multivariate model, HRQOL parameters-global health status (hazard ratio [HR], 0.97; 95% confidence interval [CI], 0.95-1.00; P < . 0001), dyspnea (HR, 1.04; 95% CI, 1.02-1.06; P < . 0002), and appetite loss (HR, 1.06; 95% CI, 1.04-1.08; P < . 0001)-were independent prognostic factors in addition to the demographic and clinical variables (all P values < .05). Adding these HRQOL variables to the clinical variables resulted in an added relative prognostic value for survival of 5%. CONCLUSIONS: These results confirm previous findings showing that baseline HRQOL scores on the EORTC QLQ-C30 provide prognostic information in addition to information from clinical measures. However, the impact of specific domains may differ across studies. Cancer 2018. © 2018 American Cancer Society.
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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 | 0.103 | 0.146 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.016 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".