78 citations · 92 across the 15 of their papers we have counts for
12 papers · 1 filter
Doubly robust estimation and sensitivity analysis for marginal structural quantile models
Chao Cheng, Liangyuan Hu, Fan Li
The marginal structure quantile model (MSQM) provides a unique lens to understand the causal effect of a time-varying treatment on the full distribution of potential outcomes. Unde…
A new method for clustered survival data]{A new method for clustered survival data: Estimation of treatment effect heterogeneity and variable selection
Liangyuan Hu
We recently developed a new method riAFT-BART to draw causal inferences about population treatment effect on patient survival from clustered and censored survival data while accoun…
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…
Propensity Score Weighting Analysis of Survival Outcomes Using Pseudo-observations
Shuxi Zeng, Fan Li, Liangyuan Hu
Survival outcomes are common in comparative effectiveness studies and require unique handling because they are usually incompletely observed due to right-censoring. A ``once for al…
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…
Estimation of causal effects of multiple treatments in healthcare database studies with rare outcomes
Liangyuan Hu, Chenyang Gu
The preponderance of large-scale healthcare databases provide abundant opportunities for comparative effectiveness research. Evidence necessary to making informed treatment decisio…
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…
The Estimation of Causal Effects of Multiple Treatments in Observational Studies Using Bayesian Additive Regression Trees
Chenyang Gu, Michael J. Lopez, Liangyuan Hu
There is currently a dearth of appropriate methods to estimate the causal effects of multiple treatments when the outcome is binary. For such settings, we propose the use of nonpar…