4 citations · 5 across the 2 of their papers we have counts for
5 papers · 1 filter
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…
Development of a Common Patient Assessment Scale across the Continuum of Care: A Nested Multiple Imputation Approach
Chenyang Gu, Roee Gutman
Evaluating and tracking patients' functional status through the post-acute care continuum requires a common instrument. However, different post-acute service providers such as nurs…