Impact of weighted composite compared to traditional composite endpoints for the design of randomized controlled trials
Bibliographic record
Abstract
Composite endpoints are commonly used in cardiovascular clinical trials. When using a composite endpoint a subject is considered to have an event when the first component endpoint has occurred. The use of composite endpoints offers the ability to incorporate several clinically important endpoint events thereby augmenting the event rate and increasing statistical power for a given sample size. One assumption of the composite is that all component events are of equal clinical importance. This assumption is rarely achieved given the diversity of component endpoints included. One means of adjusting for this diversity is to adjust the outcomes using severity weights determined a priori. The use of a weighted endpoint also allows for the incorporation of multiple endpoints per patient. Although weighting the outcomes lowers the effective number of events, it offers additional information that reduces the variance of the estimate. We created a series of simulation studies to examine the effect on power as the individual components of a typical composite were changed. In one study, we noted that the weighted composite was able to offer discriminative power when the component outcomes were altered, while the traditional method was not. In the other study, we noted that the weighted composite offered a similar level of power to the traditional composite when the change was driven by the more severe endpoints.
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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 | 0.436 | 0.700 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".