Health-related quality of life outcomes reporting associated with FDA approvals in haematology and oncology
Bibliographic record
Abstract
Objective: Health-related quality of life (HRQoL) outcomes are important in making clinical and policy decisions. This study aimed to examine the HRQoL reporting in cancer drug trials leading to Food and Drug Administration (FDA) approvals. Methods and analysis: This retrospective cohort study analysed HRQoL data for trials leading to FDA approvals between July 2015 and May 2020. Proportion of included trials that reported HRQoL, latency between FDA approval and first report of HRQoL data, HRQoL outcomes, and their correlation with OS (overall survival) and PFS (progression-free survival) were analysed. Results: Of the 233 trials associated with 207 FDA approvals, HRQoL was reported in 50% of trials, of which only 42% had the data reported by the time of FDA approval. There were no changes in frequency of HRQoL reporting between 2015 and 2020. HRQoL data were first reported in the primary publication in only 30% trials. Of the 115 trials with HRQoL data available, HRQoL improved in 43%, remained stable in 53% and worsened in 4% of trials. Among the trials that led to FDA approvals based on surrogate endpoints (79%), HRQoL was reported in 45% and improved only in 18% trials. There was no association between OS and PFS benefit and HRQoL outcomes. Conclusion: Rates of HRQoL reporting were suboptimal in trials that led to FDA approvals with no improvements seen between 2015 and 2020. HRQoL reporting was often delayed and not presented in the primary publication. HRQoL reporting was further sparse in trials with approvals based on surrogate endpoints and HRQoL improved in only a minority of them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".