Exploring factors associated with views on sharing of certain interim trial result measures by the data safety monitoring board (DSMB) with non-DSMB members
Bibliographic record
Abstract
BACKGROUND: Sharing interim result measures by the Data Safety Monitoring Board (DSMB) with non-DSMB members is an important issue that can affect trial integrity. Currently, it is unclear if there are demographic factors associated with sharing such information. This study's objective is to primarily explore the demographic factors associated with the DSMB sharing certain interim result measures and secondarily, explore demographic factors associated with the perceived usefulness in sharing certain interim result measures, with non-DSMB members. METHODS: We 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 the DSMB sharing interim trial results, specifically the interim control event rate (IControlER), interim combined even rate (ICombinedER), adaptive conditional power (ACP) and unconditional conditional power (UCP) with non-DSMB members. Binary logistic and multiple linear regressions were used to explore if demographic factors were associated with sharing a certain interim result measure and the perceived usefulness of sharing that interim result measure, respectively. Multiple imputation (MI) was used to evaluate the impact of missing data as a sensitivity analysis. RESULTS: Approximately 3136 (936 from SCT + ~ 2200 from ISCB) members were invited (response rate of 12%; [371/3136]. Two main findings: (1) involvement in more than 15 private industry-sponsored trials was associated with not endorsing the sharing of the IControlER (odds ratio [OR] = 2.92; 95% confidence interval [CI]: 1.31, 6.52]; p = 0.012), and (2) involvement in more than 15 private industry-sponsored trials was associated positively with an increase in the perceived usefulness in sharing the ACP by 2.35 points (beta coefficient estimate = 2.35 [95% CI: 0.45, 4.05], p = 0.017. The findings were similar after sensitivity analyses. CONCLUSIONS: An individual involved with more than 15 trials that had some form of private industry sponsorship is a demographic factor associated with NOT sharing the IControlER by the DSMB and an increased perceived usefulness in sharing the ACP at interim. Further studies are needed to evaluate for these demographic factors given the limitations of this study related to missing data. Due to some key limitations, regarding high non-response and missing data, we caution interpreting the results as definitive, but rather look at them as a first exploratory step to find potential associations for further evaluation.
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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 | 0.031 | 0.120 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".