Sharing some interim data in trial monitoring can mislead or unmask trial investigators: A scenario-based survey of trial experts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Sharing masked interim results by the Data Safety Monitoring Board (DSMB) with non-DSMB members is an important issue that can affect trial integrity. Our survey's objective is to collect evidence to understand how seemingly masked interim results or result extrapolations are interpreted and discuss whether these results should be shared at interim. METHODS: Conducted a 6 scenario-question survey asking trial experts how they interpreted three kinds of seemingly masked interim results or result extrapolation measures (interim combined event rate, adaptive conditional power and "unconditional" conditional power). RESULTS: Thirty-one current Consolidated Standards of Reporting Trials group affiliates were invited for survey participation (February 2015). Response rate: 71.0% (22/31). About half, 52.6% (95% CI: 28.9%-74.0%), (10/19), correctly indicated that the interim combined event rate can be interpreted in three ways (drug X doing better than placebo, worse than placebo or the same) if shared at interim. The majority, 72.2% (95% CI: 46.5%-89.7%), (13/18), correctly indicated that the adaptive conditional power suggests relative treatment group effects. The majority, 53.3% (95% CI: 26.6%-77.0%), (8/15), incorrectly indicated that the "unconditional" conditional power suggests relative treatment group effects. DISCUSSION/CONCLUSION: Knowledge of these three results or result extrapolation measures should not be shared outside of the DSMB at interim as they may mislead or unmask interim results, potentially introducing trial bias. For example, the interim combined event rate can be interpreted in one of three ways potentially leading to mistaken guesswork about interim results. Knowledge of the adaptive conditional power by non-DSMB members is telling of relative treatment effects thus unmasking of interim results.
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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 | 0.164 | 0.906 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.014 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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 it