3 papers
stat.ME2025
A decision-theoretic framework for uncertainty quantification in epidemiological modelling
Nicholas Steyn, Freddie Bickford Smith, Cathal Mills +3
Estimating, understanding, and communicating uncertainty is fundamental to statistical epidemiology, where model-based estimates regularly inform real-world decisions. However, sou…
stat.ME2025
Targeting relative risk heterogeneity with causal forests
Vik Shirvaikar, Andrea Storås, Xi Lin +1
The identification of heterogeneous treatment effects (HTE) across subgroups is of significant interest in clinical trial analysis. Several state-of-the-art HTE estimation methods,…
stat.ME2025
A general framework for probabilistic model uncertainty
Vik Shirvaikar, Stephen G. Walker, Chris Holmes
Existing approaches to model uncertainty typically either compare models using a quantitative model selection criterion or evaluate posterior model probabilities having set a prior…