Model-Assisted Bayesian Estimators for Ordinal Outcomes in RCTs
Published:Dec 30, 2025 19:53
•1 min read
•ArXiv
Analysis
This paper addresses the limitations of traditional methods (like proportional odds models) for analyzing ordinal outcomes in randomized controlled trials (RCTs). It proposes more transparent and interpretable summary measures (weighted geometric mean odds ratios, relative risks, and weighted mean risk differences) and develops efficient Bayesian estimators to calculate them. The use of Bayesian methods allows for covariate adjustment and marginalization, improving the accuracy and robustness of the analysis, especially when the proportional odds assumption is violated. The paper's focus on transparency and interpretability is crucial for clinical trials where understanding the impact of treatments is paramount.
Key Takeaways
- •Proposes new, transparent summary measures for ordinal outcomes in RCTs.
- •Develops model-assisted Bayesian estimators for these measures.
- •Addresses the limitations of proportional odds models, especially when the proportional odds assumption is violated.
- •Provides a weighting scheme with appealing invariance properties.
- •Demonstrates good performance through simulations and a real-world example (COVID-OUT trial).
Reference
“The paper proposes 'weighted geometric mean' odds ratios and relative risks, and 'weighted mean' risk differences as transparent summary measures for ordinal outcomes.”