78 citations · 86 across the 8 of their papers we have counts for
8 papers
A flexible approach for causal inference with multiple treatments and clustered survival outcomes
Liangyuan Hu, Jiayi Ji, Ronald D. Ennis +1
When drawing causal inferences about the effects of multiple treatments on clustered survival outcomes using observational data, we need to address implications of the multilevel d…
CIMTx: An R package for causal inference with multiple treatments using observational data
Liangyuan Hu, Jiayi Ji
CIMTx provides efficient and unified functions to implement modern methods for causal inferences with multiple treatments using observational data with a focus on binary outcomes.…
Estimating the causal effects of multiple intermittent treatments with application to COVID-19
Liangyuan Hu, Jiayi Ji, Himanshu Joshi +2
To draw real-world evidence about the comparative effectiveness of multiple time-varying treatments on patient survival, we develop a joint marginal structural survival model and a…
A flexible approach for variable selection in large-scale healthcare database studies with missing covariate and outcome data
Jung-Yi Joyce Lin, Liangyuan Hu, Chuyue Huang +2
Prior work has shown that combining bootstrap imputation with tree-based machine learning variable selection methods can provide good performances achievable on fully observed data…
Variable selection with missing data in both covariates and outcomes: Imputation and machine learning
Liangyuan Hu, Jung-Yi Joyce Lin, Jiayi Ji
The missing data issue is ubiquitous in health studies. Variable selection in the presence of both missing covariates and outcomes is an important statistical research topic but ha…
A flexible sensitivity analysis approach for unmeasured confounding with multiple treatments and a binary outcome with application to SEER-Medicare lung cancer data
Liangyuan Hu, Jungang Zou, Chenyang Gu +3
In the absence of a randomized experiment, a key assumption for drawing causal inference about treatment effects is the ignorable treatment assignment. Violations of the ignorabili…
Estimating heterogeneous survival treatment effect in observational data using machine learning
Liangyuan Hu, Jiayi Ji, Fan Li
Methods for estimating heterogeneous treatment effect in observational data have largely focused on continuous or binary outcomes, and have been relatively less vetted with surviva…
Estimation of Causal Effects of Multiple Treatments in Observational Studies with a Binary Outcome
Liangyuan Hu, Chenyang Gu, Michael Lopez +2
There is a dearth of robust methods to estimate the causal effects of multiple treatments when the outcome is binary. This paper uses two unique sets of simulations to propose and…