Hierarchical End Points in Prior Heart Failure Trials and the HEART-FID Trial
Bibliographic record
Abstract
BACKGROUND: Clinical trials in heart failure (HF) traditionally use time-to-event analyses focusing on death and hospitalization for HF. These time-to-first event analyses may have more limited abilities to assess the probability of benefiting from a therapy, especially if that benefit manifests as improved functional status rather than reduced risk of death or HF hospitalization. Hierarchical end points including clinical outcomes and patient status measures allow for ranked evaluation of outcomes in 1 metric assessing whether patients randomized to intervention or control are more likely to derive an overall benefit while also allowing more patients to contribute to the primary outcome. METHODS: We review the rationale for using hierarchical end points in HF trials, provide examples of HF trials that used this type of end point, and discuss its use in the HEART-FID trial (Randomized Placebo-Controlled Trial of Ferric Carboxymaltose as Treatment for Heart Failure With Iron Deficiency), the largest HF trial to date implementing a hierarchical end point analysis for the primary outcome. RESULTS: Using a hierarchical end point as the primary outcome allows for the inclusion of different types of outcomes in 1 ranked end point, making it possible to more holistically assess the potential utility of a new therapy on patient well-being and outcomes. CONCLUSIONS: Hierarchical end points assess the potential utility of a new therapy on patient well-being and outcome more holistically than time-to-first event analysis. Trials that would not have been feasible due to decreasing rates of death and hospitalization in the HF population can use hierarchical end points to successfully power studies to identify promising HF therapies. The HEART-FID trial used hierarchical end points to better determine the role of intravenous ferric carboxymaltose in patients with HF. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03037931.
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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 | 0.109 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".