Evaluating the benefit of event adjudication of cardiovascular outcomes in large simple RCTs
Bibliographic record
Abstract
BACKGROUND: Event adjudication in randomized controlled trials is thought to be a necessary step to remove noise and potential bias from the results [1,2]. However, this hypothesis has not been widely evaluated. We conducted a meta-analysis of a series of cardiovascular outcomes trials and estimated the effect of adjudication on treatment estimates and on the number of outcomes included the trials. METHODS: Data were retrieved from all cardiovascular outcomes trials conducted at the Population Health Research Institute (PHRI) between 1993 and 2006. These data included 10 trials with over 9000 events from 95,038 individuals. Differences in the log odds ratios between adjudicated and reported outcomes were analyzed and summarized using a ratio of odds ratios. Both masked and unmasked trials were included in this analysis. RESULTS: There were no effects of event adjudication on the estimates of treatment effect for the primary outcomes, myocardial infarction (MI), stroke, or cardiovascular/vascular death. For the trial primary outcomes, the effect of adjudication vs. reported events was OR ratio = 1.00 [95% confidence interval (CI): 0.97-1.02]. There were also no significant differences in the number of outcomes included in the trials. Results were the same for masked and unmasked trials. LIMITATIONS: The number of unmasked trials were small, and this analysis was restricted to cardiovascular endpoints reported from trials managed by a single coordinating center, with similar sets of procedures. Individual patient data were not used for the analysis. CONCLUSIONS: This systematic meta-analysis failed to detect any effect of event adjudication on study conclusions and the numbers of events included in the final analyses were minimally changed. Given the considerable effort required to perform adjudication, there is a need to demonstrate that this process does indeed increase the sensitivity of trials. There is a need to conduct more systematic analyses of the effect of event adjudication in other trials to determine if this process is truly worthwhile.
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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 | 0.490 | 0.765 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.027 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".