Association between progression-free survival and health-related quality of life in oncology: A systematic review and regression analysis.
Bibliographic record
Abstract
6574 Background: The goal of cancer care is to improve not only survival duration but also health-related quality of life (HRQoL). Progression-free survival (PFS) has become an important surrogate outcome in assessing efficacy of new cancer drugs, but the relationship between improved PFS and HRQoL is not clear, particularly in the absence of an overall survival (OS) benefit. The objective of this study was to examine the relationship between PFS and HRQoL through a systematic review and analysis of published evidence. Methods: We searched MEDLINE, Embase, and Cochrane databases for randomized controlled human trials addressing oncology treatments published since 2000. We utilized the difference in median PFS time duration between treatment groups, with eligible trials being those reporting no significant OS benefit. We calculated and compared HRQoL between treatment groups using the difference in standardized mean incremental area under the curve adjusted to per month values. Weighted simple regressions were used to examine the PFS-HRQoL association, separately for physical, emotional, and global HRQoL domains. Results: 35,960 citations were identified, with 42 final articles reporting 30 clinical trials being eligible for inclusion. The 30 trials involved 10,731 patients across 12 types of cancer using 6 different instruments. 67% of all trials had improved PFS, and 56%, 54%, and 62% of trials had improved physical, global, and emotional HRQoL, respectively. The PFS with physical domain (n = 18) regression coefficient (slope) β = -0.205 (95% CI; -0.649 to 0.239), with emotional domain (n = 13) β = 0.775 (95% CI; -0.048 to 1.598), and with global domain (n = 24) β = 0.094 (95% CI; -0.271 to 0.459). Conclusions: Our systematic review and analyses revealed weak and nonsignificant association between PFS and HRQoL. In the absence of OS benefit, when longer PFS doesn’t correspond to better HRQoL, using PFS as the proxy for efficacy for oncology drugs is problematic.
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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 | 0.022 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.020 |
| Bibliometrics | 0.015 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".