Associations between safety, tolerability, and toxicity and the reporting of health‐related quality of life in phase III randomized trials in common solid tumors
Bibliographic record
Abstract
BACKGROUND: Anti-cancer drugs are approved typically on the basis of efficacy and safety as evaluated in phase III randomized trials (RCTs). Health-related quality of life (HRQoL) is a direct measure of patient benefit, but is under-reported. Here we explore associations with reporting of HRQoL data in phase III RCTs in common solid tumors. METHODS: We searched ClinicalTrials.gov to identify phase III RCTs evaluating new drugs in adults with advanced cancers that completed accrual between January 2005 and October 2016. Data on HRQoL, safety, and tolerability comprising treatment-related death, treatment discontinuation and commonly reported grade 3 or 4 adverse events (AEs) were extracted. Associations between these measures and reporting of HRQoL data were explored using logistic regression. RESULTS: Of 377 phase III RCTs identified initially, 143 studies were analysed and comprised 55% positive trials and 90% industry sponsored trials. HRQoL was listed as an endpoint in 59% trials; and of these, only 65% reported HRQoL data. There were higher odds of reporting HRQoL data for positive trials (OR 2.05, P = .04) and trials published in journals with higher impact factor (OR 1.35, P = .01). Reporting of HRQoL was not associated with treatment-related death (OR 1.25, P = .40) or treatment discontinuation (OR 1.12, P = .61), but was positively associated with dyspnea and dermatological adverse events. CONCLUSIONS: HRQoL is reported in only two-thirds of RCTs that describe collecting such data. Reporting of HRQoL is associated with positive trial outcome and higher journal impact factor, but not associated with overall safety and tolerability of anti-cancer drugs.
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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 | 0.472 | 0.684 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.013 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".