BackgroundIn 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.MethodThis 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.ResultsIn 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).ConclusionsHigh 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 registrationORIGIN (NCT00069784), COMPASS (NCT01776424), TRANSCEND (NCT00153101).