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…
stat.ME2026
A longitudinal Bayesian framework for estimating causal dose-response relationships
Yu Luo, Kuan Liu, Ramandeep Singh +1
Existing causal methods for time-varying exposure and time-varying confounding focus on estimating the average causal effect of a time-varying binary treatment on an end-of-study o…
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…