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  Effective Semiparametric Analysis for Causal Inference with Time-to-Event Outcomes 

  Wu, Tianmin (Georgetown University, 2023)

  Medical studies frequently encounter imbalanced treatment assignments, which may lead to biased estimates of treatment effects. To address this issue and obtain unbiased estimates, the application of causal inference methods ...
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  Functional Regression Models for Gene-based Association Analysis of Complex Traits 

  Wang, Shuqi (Georgetown University, 2023)

  Gene-based association studies can identify gene regions that contain multiple genetic risk variants with small and moderate effects for complex traits. In my research, I develop functional ordinal logistic regression ...
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  Network Analysis in Human Microbiome Study 

  Li, Shilan (Georgetown University, 2023)

  Microorganisms coexist with complex interacting relationships that form dynamic stability and support health or cause a disease. Characterizing microbial interactions by statistical network analyses is useful to better ...
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  Regularized Local Smoothing for Longitudinal Analysis with Time-Varying Coefficient Models and Transformation Models 

  Ma, Xiaoyang (Georgetown University, 2022)

  This dissertation concerns selecting locally influential variables in both conditional mean and conditional distribution-based models with high-dimensional longitudinal data. Motivated by a large epidemiological study for ...
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  Highly Robust Semiparametric Method and Its Applications in Biomedical Studies 

  Yin, Anqi (Georgetown University, 2022)

  Causal inference has garnered renewed attention as the result of increased demand in comparative observational studies, and clinical trials utilizing real-world data, which are playing an increasingly more important role ...

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