Adherence of published randomized phase 3 cancer trials to principles proposed by common-sense oncology.
Bibliographic record
Abstract
11019 Background: Randomized clinical trials (RCTs) remain the gold standard for evaluating the efficacy and safety of novel cancer therapies. Some RCTs are well-designed and show meaningful improvements in patient outcomes while others are confounded by various types of bias, or do not reflect outcomes that matter to patients. Published RCTs should be designed, analyzed and reported to provide optimal, unbiased information for clinicians to enhance treatment decision-making Common-Sense Oncology (CSO) is an initiative of clinicians, patient advocates, researchers, and policymakers with the mission of ensuring that cancer care and research are focused on outcomes that matter to patients. CSO has published a checklist for the design, analysis and reporting of RCTs evaluating systemic treatments for cancer. In the present study, we have applied the checklists to a cohort of cancer drug trials to assess the extent to which CSO principles were incorporated in reports of RCTs published in 2023 in high-impact journals. Methods: We reviewed retrospectively phase 3 RCTs evaluating systemic therapies for adult solid tumors published in 2023 in The New England Journal of Medicine, Lancet, Lancet Oncology, JAMA, JAMA Oncology, Journal of Clinical Oncology, and Annals of Oncology . These journals were selected based on their high impact. For each trial we evaluated the trial design in the methodology, how the results were reported and the discussion section using the CSO RCT Checklist. Results: 50 RCTs evaluating systemic therapies for solid tumors were published in 2023. The most common tumor types were lung, liver, and prostate cancer. Progression-free survival and overall survival were the primary endpoints in 44% (22) and 42% (21) of trials, respectively. Only 36/50 trials justified the control arm, 25/50 justified the primary endpoint, and 18 included Quality-of-Life as a secondary endpoint. Only two trials addressed strategies to limit censoring and dropout; numbers of censored patients (with numbers at risk) were shown under Kaplan-Meier curves in only 21/50 trials, and sensitivity analysis to determine the potential effects of censoring was done in only 5 trials. Chronic toxicities were reported in only one trial, only 7 trials included patient-reported outcomes and only 3/50 trials mentioned cost of the drug. Conclusions: Our findings underscore the need for standardized methodologies, comprehensive design, and reporting in oncology RCTs. By identifying gaps in RCT design and reporting, CSO aims to improve the quality and consistency of future trials.
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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 | 0.890 | 0.947 |
| Meta-epidemiology (narrow) | 0.003 | 0.009 |
| Meta-epidemiology (broad) | 0.011 | 0.016 |
| Bibliometrics | 0.027 | 0.026 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.012 | 0.019 |
| Research integrity | 0.026 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".