Research Interest
Research Interest
My research primarily focuses on Nonparametric Bayesian Averaging and Selection, Selective Inference, Bayesian Data Integration and Heterogenous Treatment Effect (HTE) Disocovery. I am deeply passionate about developing and applying statistical methodologies to clinical trial data, aiming to derive meaningful conclusions that advance scientific knowledge and inform decision-making.
Moving forward, I plan to contribute to the following areas:
(1) Discovering frequentist properties of Bayesian adaptive trial designs: Investigating the frequentist properties of Bayesian adaptive trial designs, particularly in the context of selective inference, to ensure rigorous and reliable decision-making.
(2) Exploring Bayesian integration methods: Developing innovative Bayesian approaches to integrate real-world data into clinical trials (e.g., real-world control arms), enhancing the applicability and generalizability of trial results.
(3) Discovering heterogeneous treatment effects for chronic diseases: Identifying heterogeneous treatment effects for chronic diseases, leveraging surrogate clinical outcomes to provide more personalized and actionable insights.
(4) Building Landmark Models with Deep Neural Networks: Leveraging longitudinal outcomes to construct landmark models, improving liver transplant allocation systems (e.g., OPOM model) and optimizing patient outcomes through the application of deep learning techniques.
Publications
Bayesian Data Integration
Tianyu Pan, Xiang Zhang, Weining Shen, and Ting Ye (2024). A Bayesian Approach for Selecting Relevant External Data (BASE): Application to a study of Long-Term Outcomes in a Hemophilia Gene Therapy Trial.
HTE Discovery for Chronic Diseases
Tianyu Pan, Maria Montez-Rath, Manjula Kurella Tamura, Lu Tian, and Vivek Charu (2024). Discovering Heterogeneous Treatment Effects on Slope-based Endpoints in Chronic Kidney Disease Trials. Accepted by BMC Medical Research Methodology.
Selective Inference
Tianyu Pan, Vivek Charu, and Lu Tian (2024). Conditional Inference for Secondary Outcomes Based on Primary Outcome Significance in Clinical Trials.
Bayesian Model Averaging and Selection
Tianyu Pan, Weining Shen, Clintin P. Davis-Stober, and Guanyu Hu (2023). Precision education: A Bayesian nonparametric approach for handling item and examinee heterogeneity in assessment data. British Journal of Mathematical and Statistical Psychology.
Tianyu Pan, Weining Shen, and Guanyu Hu (2022). Clustering spatial functional data using a geographically weighted Dirichlet Process. Canadian Journal of Statistics.
Tianyu Pan, Guanyu Hu, and Weining Shen (2021). Identifying latent groups in spatial panel data using Markov random field constrained product partition model. Statistica Sinica.
Tianyu Pan, Weining Shen, and Guanyu Hu (2022). Bi-directional clustering via Averaged Mixture of Finite Mixtures.
Collaborative Works
Danielle Brazel, Priyanka Kumar, Hung Doan, Tianyu Pan, Weining Shen, Ling Gao, and Justin T. Moyers (2023). Genomic Alterations and Tumor Mutation Burden in Merkel Cell Carcinoma. JAMA Network Open, 6(1), e2249674-e2249674.
Sloan A Lewis, Brianna Doratt, Suhas Sureshchandra, Tianyu Pan, Steven W Gonzales, Weining Shen, Kathleen A Grant, and Ilhem Messaoudi (2022). Profiling of extracellular vesicle‐bound miRNA to identify candidate biomarkers of chronic alcohol drinking in nonhuman primates. Alcoholism: Clinical and Experimental Research, 46(2), 221-231.
Other Peer-Reviewed Works
Tong Zou, Tianyu Pan, and Hal Stern (2021). Recognition of overlapping elliptical objects in a binary image. Pattern Analysis and Applications, 24(3), 1193-1206.
Wei Hu, Tianyu Pan, Dehan Kong, and Weining Shen (2021). Nonparametric matrix response with application to brain imaging data analysis. Biometrics, 77(4), 1227-1240.