5 papers
Doubly robust estimators of the restricted mean time in favor estimands in individual- and cluster-randomized trials
Xi Fang, Bingkai Wang, Guangyu Tong +3
Progressive multi-state survival outcomes are common in trials with recurrent or sequential events and require treatment effect estimands that remain interpretable without proporti…
Calibrated Bayes analysis of cluster-randomized trials
Ruyi Liu, Joshua L. Warren, Yuki Ohnishi +3
In cluster-randomized trials (CRTs), entire clusters of individuals are randomized to treatment, and outcomes within a cluster are typically correlated. While frequentist approache…
Estimands and doubly robust estimation for cluster-randomized trials with survival outcomes
Xi Fang, Bingkai Wang, Liangyuan Hu +1
Cluster-randomized trials (CRTs) are experimental designs where groups or clusters of participants, rather than the individual participants themselves, are randomized to interventi…
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…
A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding
Xinyuan Chen, Liangyuan Hu, Fan Li
In longitudinal observational studies with time-to-event outcomes, a common objective in causal analysis is to estimate the causal survival curve under hypothetical intervention sc…