78 citations · 86 across the 8 of their papers we have counts for
4 papers · 1 filter
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…