Sharing some interim data in trial monitoring can mislead or unmask trial investigators: A scenario-based survey of trial experts
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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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 | 0.244 | 0.389 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".