3 papers
stat.ME2026
Bayesian hierarchical bootstrap framework for causal subgroup estimation with a time-to-event outcome
Mengyao Shi, Amanda Ricciuto, Mark Deneau +1
Causal estimation of treatment effects within prespecified subgroups, such as biomarker-defined strata, disease phenotypes, or demographic groups are often of clinical interest. Ba…
cs.LG2026
Machine learning models for estimating counterfactuals in a single-arm inflammatory bowel disease study
Dan Liu, Fida K. Dankar, Jennifer C. deBruyn +4
Single-arm trials accelerate study timelines by reducing the number of patients that must be recruited for a concurrent control group. However, these designs require an alternative…
stat.ME2025
Bayesian Sensitivity Analysis for Causal Estimation with Time-varying Unmeasured Confounding
Yushu Zou, Liangyuan Hu, Amanda Ricciuto +2
Causal inference relies on the untestable assumption of no unmeasured confounding. Sensitivity analysis can be used to quantify the impact of unmeasured confounding on causal estim…