A comparison of approaches for adjudicating outcomes in clinical trials
Bibliographic record
Abstract
BACKGROUND: Incorrect classification of outcomes in clinical trials can lead to biased estimates of treatment effect and reduced power. Ensuring appropriate adjudication methods to minimize outcome misclassification is therefore essential. While there are many reported adjudication approaches, there is little consensus over which approach is best. METHODS: Under the assumption of non-differential assessment (i.e. that misclassification rates are the same in each treatment arm, as would typically be the case when outcome assessors are blinded), we use simulation and theoretical results to address four different questions about outcome adjudication: (a) How many assessors should be used? (b) When is it better to use onsite or central assessment? (c) Should central assessors adjudicate all outcomes, or only suspected events? (d) Should central assessment with multiple assessors be done independently or through group consensus? RESULTS: No one adjudication approach performs optimally in all settings. The optimal approach depends on the misclassification rates of site and central assessors, and the correlation between assessors. We found: (a) there will generally be little incremental benefit to using more than three assessors and, for outcomes with very high correlation between assessors, using one assessor is sufficient; (b) when choosing between site and central assessors, the assessor with the smallest misclassification rate should be chosen; when these rates are unknown, a combination of one site assessor and two central assessors will provide good results across a range of scenarios; (c) having central assessors adjudicate only suspected events will typically increase bias, and should be avoided, unless the threshold for sending outcomes for central assessment is extremely low; (d) central assessors can adjudicate either independently or in a group, and the preferred option should be dictated by whichever is expected to have the lowest misclassification rate. CONCLUSIONS: Outcome adjudication is of critical importance to ensure validity of trial results, although no one approach is optimal across all settings. Investigators should choose the best strategy based on the specific characteristics of their trial. Regardless of the adjudication strategy chosen, assessors should be qualified and receive appropriate training.
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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 | 0.694 | 0.825 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.017 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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".