Making Sense of Hierarchical Composite End Points in Randomized Clinical Trials—A Primer for Infectious Diseases Clinicians and Researchers
Bibliographic record
Abstract
Hierarchical composite end points (HCEs), combining features of simple composite end points and conventional ordinal end points, are increasingly being used in infectious diseases (ID) research. However, many clinicians may be unfamiliar with these novel end points, including the variety of different target parameters that may be of interest and the methods that can be used to estimate them. In this review, we provide a conceptual overview of HCEs by defining them and providing examples from the ID literature. We explain different methods for analyzing HCEs, including (1) the Wilcoxon rank sum approach (often used in studies with a desirability of outcome ranking [DOOR] end point), (2) generalized pairwise comparisons (used to estimate a win ratio or win odds), (3) proportional odds model (and the relevance of the proportional odds assumption), and, (4) the probabilistic index model. This review will help ID clinicians and healthcare providers interpret current and future research using such end points.
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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 | 0.579 | 0.692 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.008 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.003 | 0.034 |
| Scholarly communication | 0.025 | 0.028 |
| Open science | 0.013 | 0.012 |
| Research integrity | 0.016 | 0.045 |
| Insufficient payload (model declined to judge) | 0.003 | 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".