Health-related quality of life in randomized controlled trials: A systematic review of prognostic significance
Bibliographic record
Abstract
Background: A landmark systematic review (Gotay et al., 2008) found that baseline patient-reported physical functioning (PF) and global quality of life (QL) independently predicted overall survival (OS) in cancer patients. Moreover, the prognostic significance of PF was supported by a meta-analysis of 10,108 patients (Quinten et al., 2009). Despite these results, health-related quality of life (HRQOL) is rarely used as predictive or stratification factor in randomized clinical trials (RCTs) and practice. The prior review was updated by examining the extent to which previously reported and possibly other HRQOL domains show prognostic value across different cancer types. A methodological evaluation assessing the implementation of analysis methods was also undertaken. Methods: A systematic review was conducted following the Cochrane methodology and including RCTs from 2006-present. Inclusion criteria were phase II, III or IV RCTs including at least one multivariate analysis examining the relationship between baseline HRQOL and OS while controlling for other clinical factors. Studies were reviewed and assessed by two independent raters using predefined criteria. Results: Forty-eight RCTs (n = 24,777) were included. These were mostly phase III studies (77%) across 13 cancer types. Studies of lung (21%) and head and neck (12%) cancers were the most prevalent. Cox proportional hazards models were most frequently used to assess the prognostic value of HRQOL (96%). In the majority of the RCTs (94%), at least one HRQOL domain was significantly associated with OS (p < .05) even after controlling for other clinical variables. PF (39%) and QL (35%) were commonly reported as independent prognostic factors in nine and eight types respectively. Methodological evaluation found that few studies followed rigorous methods. Conclusions: Our results build upon previous findings confirming the prognostic significance of PF and QL and further highlight its prognostic value across the majority of disease types. Given this, we recommend that these scales be used to supplement available clinical staging and for stratification. A more consistent use of methods would allow for stronger conclusion regarding predictive accuracy. Legal entity responsible for the study: EORTC. Funding: Fonds Cancer (FOCA). Disclosure: A. Bottomley: Unrestricted education grants from Merck and Boehringer-Ingelheim. All other authors have declared no conflicts of interest.
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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 | 0.067 | 0.253 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.019 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".