Survival Proportional Odds Outperforms Cox Regression in High-Heterogeneity Heart Failure Trials: Insights from DAPA-HF and DELIVER

Introduction: The Challenge of Heterogeneity in Heart Failure Trials

In the landscape of clinical cardiology, heart failure (HF) remains one of the most complex conditions to study due to the vast diversity in patient presentations and clinical trajectories. Whether categorized by reduced ejection fraction (HFrEF) or preserved ejection fraction (HFpEF), patients within these cohorts exhibit significant risk heterogeneity. This variation in baseline risk—driven by comorbidities, age, and disease severity—poses a substantial challenge for biostatisticians and clinicians attempting to measure the true efficacy of new therapeutic interventions.

For decades, the Cox proportional hazards (PH) model has served as the gold standard for analyzing time-to-event outcomes in cardiovascular trials. However, emerging evidence suggests that in populations with high risk heterogeneity, the Cox model may provide biased estimates of treatment effects. A recent study by Myte et al., published in Circulation: Heart Failure, explores a compelling alternative: the survival proportional odds (PO) model. By applying this model to data from the landmark DAPA-HF and DELIVER trials, researchers have demonstrated its potential to minimize bias and improve the interpretation of treatment benefits in heart failure.