Maintaining confidentiality of interim data to enhance trial integrity and credibility
Bibliographic record
Abstract
BACKGROUND: For clinical trials of interventions that could affect mortality or major morbidity, Data Monitoring Committees have an important role in safeguarding patient interests and enhancing trial integrity and credibility. In trials overseen by an independent DMC it is widely recognized that interim data should remain confidential to the DMC and to the statistical group preparing reports. However, we have found that the principle of confidentiality is not always followed in practice, particularly where the interim data include complete results on a short-term outcome measure. PURPOSE: To discuss the reasoning and evidence supporting the principle of confidentiality of interim data with emphasis on the setting where the interim data include complete results on a short-term outcome. METHODS: We review the reasons why wider access to interim data can increase the risk of false positive or false negative conclusions and discuss the types of harm which can occur. We provide illustrations and insights from recent experiences and discuss the level of consensus in the research community. RESULTS: The arguments in favor of early release of interim data include the need to provide reliable data in a timely manner to patients and physicians, the potential to increase the enthusiasm of trial investigators, and to restore equipoise. However interim data, even where these include complete results on a short-term outcome measure, provide an unreliable and biased assessment of the overall benefit-to-risk profile of the trial treatments. Pre-judgment based on over-interpretation of such interim data can affect recruitment, treatment delivery, and follow-up, risking the ability of the trial to achieve its goals. CONCLUSIONS: In order to preserve the integrity of a trial and safeguard the interests of patients, interim data, including complete data on short-term outcomes, should remain confidential to the DMC and the statistical group responsible for preparing interim reports until the trial has achieved its primary objectives.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchResearch integrity Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Metaresearch Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.685 | 0.838 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.021 | 0.029 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.022 | 0.020 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".