Comparison of total event analysis and first event analysis in relation to heterogeneity in cardiovascular trials
Bibliographic record
Abstract
BACKGROUND: In cardiovascular (CV) trials, analyzing the total number of events, rather than just time-to-first event, enhances understanding of participants' health. Adapting Cox models to account for between-subject heterogeneity in multiple events and understanding its impact plays crucial roles in total event analysis. METHOD: This study compares effect sizes from first event and total event analyses in three cardiovascular trials: ORIGIN (N = 12,537, median follow-up of 6.2 years), COMPASS (N = 18,278, median follow-up of 1.8 years), TRANSCEND (N = 5,926, median follow-up of 1.1 years). It also examines the impact of heterogeneity, measured by the negative binomial overdispersion parameter. Treatment effects were assessed using the Cox model for first events and the negative binomial (NB), Andersen-Gill (AG), Prentice-Williams-Peterson (PWP), Wei-Lin-Weissfeld (WLW), and Lin-Wei-Yang-Ying (LWYY) models for total events. Hazard ratios (HRs) or risk ratios (RRs), 95% confidence intervals (CIs), and CI widths were reported. The risk ratio applies to negative binomial. The first composite was consisted of myocardial infarction (MI), stroke, cardiovascular death. Simulations assessed Type I error, power, and mean squared error across the different approaches. RESULTS: In ORIGIN, the incidence per 100 years increased from 2.9 to 3.8 for the first composite with a heterogeneity of 2.4. The HR or RR for the first composite was 1.03 (95% CI, 0.94-1.12, CI width = 0.18) using Cox, 1.01 (95% CI, 0.92-1.11, CI width = 0.19) for NB, 1.01 (95% CI, 0.94-1.09, CI width = 0.15) for AG, 1.02 (95% CI, 0.94-1.10, CI width = 0.16) for PWP total, 1.01 (95% CI, 0.94-1.09, CI width = 0.15) for PWP gap, 1.03 (95% CI, 0.94-1.12, CI width = 0.18) for WLW and 1.01 (95% CI, 0.92-1.11, CI width = 0.19) for LWYY. Similar trends were observed in other studies. Our simulation results showed that total event approaches had approximately 5% higher power than the Cox model, though power declined exponentially across all methods with increasing heterogeneity. Among the total event methods, AG, PWP gap, and LWYY demonstrated better power, with AG and LWYY also achieving the smallest mean squared error (MSE). CONCLUSIONS: High heterogeneity arises when a small number of patients experience a disproportionately large number of events. This effect is more pronounced when the overall event incidence is low and few patients experience any events. The effect size and CI width stayed consistent with low heterogeneity across different approaches. Power decreased with high heterogeneity. The AG and LWYY approaches slightly outperformed the other approaches. CLINICAL TRIAL REGISTRATION: ORIGIN (NCT00069784), COMPASS (NCT01776424), TRANSCEND (NCT00153101).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.319 | 0.892 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".