Abstract 5604: Refining Clinical Trial Composite Outcomes With Investigator-Assessed Weights: An Application to the ASSENT-3 Trial
Bibliographic record
Abstract
Background: Declining cardiac mortality and rising costs of clinical trials places a new priority on efficient use of all patient outcomes. Whereas traditional time-to-event analysis exclusively assigns equal weight to the first event in the composite end point, this is counterintuitive to stakeholders. Methods: We surveyed clinical investigators and asked them to assess the relative severity weights they would place on death, recurrent myocardial infarction (re-MI), cardiogenic shock and congestive heart failure (CHF) in ST-elevation myocardial infarction (STEMI) patients. Their assessment was then applied to a modified survival analysis of selected 30 day endpoints in the ASSENT-3 trial which randomized 6095 STEMI patients and compared three reperfusion strategies: tenecteplase (TNK) + unfractionated heparin (UH); TNK + enoxaparin (enox); TNK + abciximab (abx). Results: Respondents (n=23) allocated relative values from a standard point scale which was converted to a single denominator (2.0) resulting in the following weights: death 1.0, shock 0.5, CHF 0.3 & re-MI 0.2. The Table provides the event rates calculated according to the first event as well as at any time during 30-day follow-up (Panel A). In Panel B traditional first event versus weighted composites using first event and all events in each patient are shown. Note that when the traditional time-to-event analysis is re-examined with a weighted composite there is an attenuation of the excess events initially observed with UH (p=0.05 vs p=0.18). By contrast, when all events from each patient are used in the weighted composite, a trend for benefit with enox was observed (p=0.15). Conclusions: This approach adds potential value to traditional analytical techniques by more efficiently incorporating the differential value of all events in each patient thereby providing new options for future trial design and analysis. Table 1.
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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 | 0.256 | 0.334 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".