Heart failure & cardiomyopathies
Survival Odds to Minimize Risk Heterogeneity Bias in Heart Failure Trials: Application to Dapagliflozin
Authors' conclusion
In HF trials, the survival proportional odds model is a viable and more robust alternative for analyzing time to event outcomes, also providing an intuitive interpretation of the treatment effect directly linked to survival probability: improved odds of being event-free in the active group compared with the control group.
Study context
- Population
- Patients with heart failure (HF) in 2 dapagliflozin trials (DAPA-HF: HFrEF; DELIVER: mildly reduced or preserved EF)2 RCTs
- Comparison
- Survival proportional odds model vs Cox regression
Methodological detailssecondary analysis of the DAPA-HF and DELIVER randomised trials with simulation study, reanalysis of 2 RCTs
- Secondary analysis of the DAPA-HF and DELIVER randomised trials with simulation study
- Reanalysis of 2 RCTs
Primary result
As reported in the abstract
- Nonproportional hazards a larger issue in the HFpEF population of DELIVER than in the more homogeneous HFrEF population of DAPA-HF
- In simulations, survival proportional odds model more robust to heterogeneity, with higher power than Cox regression in high-heterogeneity populations; similar or slightly worse in more or less heterogeneous populations
- Reanalyses: consistently higher power with survival proportional odds model in DELIVER; similar power in DAPA-HF
Original publication
Survival Odds to Minimize Risk Heterogeneity Bias in Heart Failure Trials: Application to Dapagliflozin. Circ Heart Fail. Published 31 October 2025. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →