1 citations · 1 across the 10 of their papers we have counts for
11 papers
Heterogeneous survivor average causal effects beyond monotonicity: Applications to a clinical trial evaluating mechanical ventilation strategies
Zihan Zhu, Guangyu Tong, Fernando Godinho Zampieri +3
Clinical trials in critical care often evaluate outcomes that are truncated by death, such as time to discharge alive, for which treatment effects are not well defined among patien…
Saturation in G: simple & robust causal inference in cluster randomized trials with informative cluster sizes
Kenneth M. Lee, Michael O. Harhay, Fan Li
Cluster randomized trials (CRTs) can exhibit informative cluster sizes (ICS) where cluster size is associated with outcomes and/or treatment effects. Under ICS, the individual and…
Who's Winning? Clarifying Estimands Based on Win Statistics in Cluster Randomized Trials
Kenneth M. Lee, Xi Fang, Fan Li +1
Treatment effect estimands based on win statistics, including the win ratio, win odds, and win difference are increasingly popular targets for summarizing endpoints in clinical tri…
Design-based nested instrumental variable analysis
Zhe Chen, Xinran Li, Michael O. Harhay +1
Two binary instrumental variables (IVs) are nested if individuals who comply under one binary IV also comply under the other. This situation often arises when the two IVs represent…
Uncovering Treatment Effect Heterogeneity in Pragmatic Gerontology Trials
Changjun Li, Heather Allore, Michael O. Harhay +2
Detecting heterogeneity in treatment response enriches the interpretation of gerontologic trials. In aging research, estimating the effect of the intervention on clinically meaning…
Evaluating Informative Cluster Size in Cluster Randomized Trials
Bryan S. Blette, Zhe Chen, Brennan C. Kahan +3
In cluster randomized trials, the average treatment effect among individuals (i-ATE) can be different from the cluster average treatment effect (c-ATE) when informative cluster siz…
Randomization inference for stepped-wedge designs with noncompliance with application to a palliative care pragmatic trial
Jeffrey Zhang, Zhe Chen, Xinyuan Chen +5
While palliative care is increasingly commonly delivered to hospitalized patients with serious illnesses, few studies have estimated its causal effects. Courtright et al. (2016) ad…
Principal stratification with recurrent events truncated by a terminal event: A nested Bayesian nonparametric approach
Yuki Ohnishi, Michael O. Harhay, Guangyu Tong +1
Recurrent events often serve as key endpoints in clinical studies but may be prematurely truncated by terminal events such as death, creating selection bias and complicating causal…
Bayesian inference for cluster-randomized trials with multivariate outcomes subject to both truncation by death and missingness
Guangyu Tong, Chenxi Li, Eric Velazquez +2
Cluster-randomized trials (CRTs) on fragile populations frequently encounter complex attrition problems where the reasons for missing outcomes can be heterogeneous, with participan…
What is estimated in cluster randomized crossover trials with informative sizes? -- A survey of estimands and common estimators
Kenneth M. Lee, Andrew B. Forbes, Jessica Kasza +5
In the analysis of cluster randomized trials (CRTs), previous work has defined two meaningful estimands: the individual-average treatment effect (iATE) and cluster-average treatmen…
A Bayesian approach to the survivor average causal effect in cluster-randomized crossover trials
Dane Isenberg, Michael O. Harhay, Andrew B. Forbes +3
In cluster-randomized crossover (CRXO) trials, groups of individuals are randomly assigned to two or more sequences of alternating treatments. Since clusters serve as their own con…