Department of Biostatistics and Epidemiology
Liangyuan Hu received her PhD in Biostatistics from Brown University. Prior to joining Rutgers University, she was an Assistant Professor of Biostatistics in the Department of Population Health Science & Policy at Mount Sinai School of Medicine. Her research interests include statistical methods for causal inference, missing data and Bayesian inference, with applications in cancer, HIV/AIDS and cardiovascular diseases. She is the PI of several methods grants (R01, R21 and PCORI) developing and improving statistical methods for drawing causal inference about nonbinary treatments from data with complex structure. Her work in causal inference has won the Outstanding Statistical Application Award from the American Statistical Association in 2019.
Dr.Hu and colleagues developed a continuous-time marginal structural model and statistical methods to estimate the causal effect of treatment initiation timing on mortality from electronic health records data. The methods were applied to find out about the optimal timing of antiretroviral therapy initiation for patients who are presented with both HIV and TB. The models proffered detailed understanding of the effects of treatment timing versus treatment duration on mortality, and can be used to emulate clinical trials to compare with findings from randomized controlled trials. This work has won the Outstanding Statistical Application Award from the American Statistical Association in 2019. Funded by PCORI and NIH, Dr. Hu is leading a team to develop statistical methods to improve comparative effectiveness research in the context of multiple treatments and longitudinal treatments. Our team has developed several Bayesian machine learning based approaches that allow for drawing more accurate causal inferences about treatment effects using complex health datasets and assessing how sensitive the causal conclusions are to varying degrees of unmeasured confounding. We have created three open-source software packages, CIMTx, SAMTx and riAFTBART, available on CRAN, for fellow researchers to implement our methods.
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Google Scholar: https://scholar.google.com/citations?user=BMS4IJoAAAAJ&hl=en