Survey of professional views on sharing interim results by the Data Safety Monitoring Board (DSMB): what to share, with whom and why
Bibliographic record
Abstract
BACKGROUND: Sharing interim results by the Data Safety Monitoring Board (DSMB) with non-DSMB members is an issue that can affect trial integrity. It is unclear what should be shared. This study assesses the views of professionals to understand what interim information should be shared at interim, with whom and why. METHODS: Conducted an online survey of members of the Society of Clinical Trials (SCT) and International Society of Clinical Biostatistics (ISCB) in 2015 asking their professional views on sharing interim results. Email was used to advertise the survey and a link in the email was provided to the online survey. RESULTS: Approximately 3136 (936 SCT members + 2200 ISCB members) members were invited. The response rate was 12% (371/3136). The majority reported the Interim Control Event Rate (IControlER) (149/237; 62.9% [95% CI, 56.7-69.0%]), Adaptive Conditional Power (ACP) (144/224; 64.3% [95% CI, 58.0%-70.6%]) and the Unconditional Conditional Power (UCP) (126/208; 60.6% [95% CI, 53.9-67.2%]) should not be shared with non-DSMB members. The majority reported that the Interim Combined Event Rate (ICombinedER) (168/262; 64.1% [95% CI, 58.0-69.9%]) should be shared with non-DSMB members particularly the steering committee (SC) because it does not unmask interim results and helps with monitoring trial progress, safety, and design assumptions. CONCLUSION: The IControlER and ACP are unmasking of interim results and should not be shared. The UCP is a technical measure that is potentially misleading and also should not be shared. The ICombinedER is usually known by the SC and sponsor making it easy to determine group rates if the IControlER is known. Though most respondents thought the ICombinedER should be shared with the SC as it does not unmask relative effects between groups, we do not recommend sharing the ICombinedER as it is flawed measure that can have multiple interpretations possibly suggesting that one group is performing better, worse or the same as a comparator group, leading to guesses about how groups are doing relative to one another.
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How this classification was reachedexpand
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 | 0.049 | 0.311 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads 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".