Quantifying Withdrawal of Consent, Loss to Follow-Up, Early Drug Discontinuation, and Censoring in Oncology Trials
Bibliographic record
Abstract
BACKGROUND: Censoring due to early drug discontinuation (EDD) or withdrawal of consent or loss to follow-up (WCLFU) can result in postrandomization bias. In oncology, censoring rules vary with no defined standards. In this study, we sought to describe the planned handling and transparency of censoring data in oncology trials supporting FDA approval and to compare EDD and WCLFU in experimental and control arms. METHODS: We searched FDA archives to identify solid tumor drug approvals and their associated trials between 2015 and 2019, and extracted the planned handling and reporting of censored data. We compared the proportion of WCLFU and EDD between the experimental and control arms by using generalized estimating equations, and performed logistic regression to identify trial characteristics associated with WCLFU occurring more frequently in the control group. RESULTS: Censoring rules were defined adequately in 48 (59%) of 81 included studies. Only 14 (17%) reported proportions of censored participants clearly. The proportion of WCLFU was higher in the control group than in the experimental group (mean, 3.9% vs 2.5%; β-coefficient, -2.2; 95% CI, -3.1 to -1.3; P<.001). EDD was numerically higher in the experimental arm in 61% of studies, but there was no statistically significant difference in the proportion of EDD between the experimental and control groups (mean, 21.6% vs 19.9%, respectively; β-coefficient, 0.27; 95% CI, -0.32 to 0.87; P=.37). The proportion of EDD due to adverse effects (AEs) was higher in the experimental group (mean, 13.2% vs 8.5%; β-coefficient, 1.5; 95% CI, 0.57-2.45; P=.002). WCLFU was higher in the control group in studies with an active control group (odds ratio [OR], 10.1; P<.001) and in open label studies (OR, 3.00; P=.08). CONCLUSIONS: There are significant differences in WCLFU and EDD for AEs between the experimental and control arms in oncology trials. This may introduce postrandomization bias. Trials should improve the reporting and handling of censored data so that clinicians and patients are fully informed regarding the expected benefits of a treatment.
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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 | 0.577 | 0.710 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.013 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".