Preserving Trial Endpoint Specificity and Cause of Death Attribution in Cardiovascular Trials
Bibliographic record
Abstract
BACKGROUND: The MARINER (Medically Ill Patient Assessment of Rivaroxaban vs Placebo in Reducing Post-Discharge Venous Thrombo-Embolism Risk) trial examined the efficacy of rivaroxaban on venous thromboembolism (VTE) following discharge in high-risk medical inpatients. The trial did not meet its primary endpoint, in part due to a lesser effect of rivaroxaban on "VTE-related death" than on nonfatal VTE. OBJECTIVES: The objective of this exploratory research was to examine the impact of more specific fatal VTE definitions on trial outcome HRs through readjudication of death endpoints. METHODS: Primary source documents for the 241 deaths in the MARINER trial were reviewed by blinded adjudicators not involved with the original trial. Prespecified definitions for VTE-related death were used, and "Death of Unknown Etiology" was allowed instead of the original endpoint "Cannot rule out pulmonary embolism." Original event determinations for nonfatal events were used in this analysis. HRs and 95% CIs for rivaroxaban vs placebo were calculated for prespecified cardiovascular outcome composites. RESULTS: Rereviewed death cases showed strong concordance with original results, except deaths originally categorized as "Cannot rule out pulmonary embolism" were redistributed, largely to undetermined death (60%). The readjudicated MARINER primary endpoint using only confirmed fatal VTE events revealed a HR of 0.46 (95% CI: 0.23-0.91) vs the original HR of 0.76 (95% CI: 0.52-1.1). CONCLUSIONS: This post-hoc, exploratory analysis of endpoint design demonstrates that designing specific trial endpoints can minimize the risk of type II error. In the trial design stage, it is important to preserve endpoint specificity to allow accurate hypothesis testing. Using standardized endpoint definitions, such as for VTE-related death, across trials can help achieve this goal.
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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 | 0.597 | 0.778 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".