5 papers
A Bayesian machine learning approach to direct and spillover effects in cluster-randomized trials with noncompliance
Saurabh Bhandari, Fan Li, Michael J. Daniels
We develop a Bayesian machine learning approach for estimating direct and spillover effects in cluster-randomized trials with treatment noncompliance. To flexibly model outcomes an…
Calibrated Bayesian inference for overall and subgroup-specific causal effects in stepped-wedge cluster-randomized trials
Ruyi Liu, Yuki Ohnishi, Xi Fang +2
Stepped-wedge cluster-randomized trials (SW-CRTs) randomly assign clusters to sequences that determine when each cluster switches from control to intervention. Conventional Bayesia…
Identification and estimation of causal mechanisms in cluster-randomized trials with post-treatment confounding using Bayesian nonparametrics
Yuki Ohnishi, Michael J. Daniels, Lei Yang +1
Causal mediation analysis in cluster-randomized trials (CRTs) is essential for explaining how cluster-level interventions affect individual outcomes, yet it is complicated by inter…
Integrating tumor burden with survival outcome for treatment effect evaluation in oncology trials
Saurabh Bhandari, Michael J. Daniels, Chenguang Wang
In early-phase cancer clinical trials, the limited availability of data presents significant challenges in developing a framework to efficiently quantify treatment effectiveness. T…
A sensitivity analysis approach to principal stratification with a continuous longitudinal intermediate outcome: Applications to a cohort stepped wedge trial
Lei Yang, Michael J. Daniels, Fan Li
Causal inference in the presence of intermediate variables is a challenging problem in many applications. Principal stratification (PS) provides a framework to estimate principal c…