Predicting the failure of randomized clinical trials in radiation oncology: What can we learn?
Bibliographic record
Abstract
e18241 Background: Although randomizedcontrolled trials (RCTs) are essential to evidence-based medicine, a significant proportion of cancer RCTs fail to complete. As RCT failure is not well understood in the field of radiation oncology, we sought to review radiotherapy RCTs to identify predictors of RCT failure. Methods: We undertook a review of the trial registry ClinicalTrials.gov to assess factors influencing the completion of RCTs involving radiotherapy. Eligible trials were registered in ClinicalTrials.gov between 09/27/2007 to 12/31/2010, included radiation (brachytherapy or external beam radiotherapy) in at least one study arm, and were closed due to either successful completion or failure to complete. Data was abstracted by two independent investigators, with discrepancies settled through consensus. Univariable and multivariable logistic regression analyses were performed to determine factors predictive of RCT failure. Results: The initial search yielded 460studies. After reviewing protocol details, 138 studies met inclusion criteria, of which 41 (30%) failed to complete. Most common reasons for RCT incompletion were: lack of accrual (59%), inadequate funding (15%), drug unavailability (7%) and interim data monitoring report recommendations (7%). The highest proportion of RCT failure was observed in the most recent era (p = 0.009), with rates increasing from 11% (1987-2006) to 39% (2009-2012). On univariable analysis, independent predictors of failure included RCTs with a surgical comparator (odds ratio [OR] of failure 6.45; p = 0.010), government sponsorship (OR 3.68; p = 0.024), inclusion of a safety endpoint (OR 3.03; p = 0.014) and study start year (OR 1.18; p = 0.030). On multivariate analysis, surgical RCTs were strongly predictive of failure (OR 7.90; p = 0.015), while behavioral RCTs were less likely to fail (OR 0.14; p = 0.047). Conclusions: Rates of RCT failure in radiation oncology are high. Trials with a surgical comparator are highly prone to failure, whereas behavioral trials are more likely to succeed. These factors can help to inform the design of future RCTs, and develop strategies to mitigate the risk of failure in future radiation RCTs that include a surgical comparator.
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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 | 0.653 | 0.896 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.013 |
| Bibliometrics | 0.017 | 0.022 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.017 | 0.039 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".