Bin Nan

About

Bin Nan is a Chancellor’s Professor of Statistics at UC Irvine. His research interests encompass various areas of statistics and biostatistics, including semiparametric inference, failure time and survival analysis, longitudinal data, missing data, two-phase sampling designs, high-dimensional data analysis, and machine learning methodology. He collaborates on projects in epidemiology, bioinformatics, and brain imaging, with a focus on identifying functional connectivity in the brain to understand how different regions interact, which may provide insights into brain function. His research activities are supported by grants from the National Science Foundation and the National Institutes of Health, primarily motivated by biomedical research collaborations. Currently, he is developing new methods and theories in survival time prediction, high-dimensional statistical inference, brain imaging data analysis, longitudinal data analysis, and regression with covariates subject to detection limits. A key goal of his work is to improve human health by developing statistical and machine learning methods to evaluate risk factors, biomarkers, improve diagnosis, and find cures for human diseases, with a particular focus on Alzheimer’s disease research. He collaborates closely with the UCI Alzheimer’s Disease Research Center and the UCI Center for the Neurobiology of Learning and Memory to identify biomarkers for earlier diagnosis.

Research topics

• Artificial Intelligence
• Internal medicine
• Computer Science
• Medicine
• Medical emergency
• Algorithm
• Economic growth
• Endocrinology
• Virology
• Applied mathematics

Selected publications

• Trends in glucagon-like peptide 1 receptor agonist use, 2014 to 2022

  Journal of the American Pharmacists Association · 2023 · 178 citations

  BACKGROUND: Recent Food and Drug Administration approvals of glucagon-like peptide 1 (GLP-1) receptor agonists linked to substantial weight loss have generated interest in demand projections. However, a longitudinal analysis in a large, diverse, current, real-world database has not been published. OBJECTIVES: The study objective was to determine user frequency of GLP-1 receptor agonist products overall and by type 2 diabetes (T2D), cardiovascular disease (CVD), and overweight or obese status. Se…

  PublisherOA PDFDOI

• Medication Use Patterns in Hospitalized Patients With COVID-19 in California During the Pandemic

  JAMA Network Open · 2021 · 27 citations

  This cohort study examines trends in medication use among patients hospitalized for COVID-19–related treatment in a large US university health care system from the start of stay-at-home orders in March 2020 throughout the rest of the year.

  DOI

• Debiased lasso for generalized linear models with a diverging number of covariates

  Biometrics · 2021 · 13 citations

  Modeling and drawing inference on the joint associations between single-nucleotide polymorphisms and a disease has sparked interest in genome-wide associations studies. In the motivating Boston Lung Cancer Survival Cohort (BLCSC) data, the presence of a large number of single nucleotide polymorphisms of interest, though smaller than the sample size, challenges inference on their joint associations with the disease outcome. In similar settings, we find that neither the debiased lasso approach (va…

  DOI

• Group vs Individual Prenatal Care and Gestational Diabetes Outcomes

  JAMA Network Open · 2023-08-29 · 7 citations

  articleOpen access

  Importance: The impact of group-based prenatal care (GPNC) model in the US on the risk of gestational diabetes (GD) and related adverse obstetric outcomes is unknown. Objective: To determine the effects of the GPNC model on risk of GD, its progression, and related adverse obstetric outcomes. Design, Setting, and Participants: This is a single-site, parallel-group, randomized clinical trial conducted between February 2016 and March 2020 at a large health care system in Greenville, South Carolina.…

  PublisherOA PDFDOI

• Debiased lasso after sample splitting for estimation and inference in high‐dimensional generalized linear models

  Canadian Journal of Statistics · 2024-08-21 · 2 citations

  articleOpen accessSenior authorCorresponding

  We consider random sample splitting for estimation and inference in high dimensional generalized linear models, where we first apply the lasso to select a submodel using one subsample and then apply the debiased lasso to fit the selected model using the remaining subsample. We show that a sample splitting procedure based on the debiased lasso yields asymptotically normal estimates under mild conditions and that multiple splitting can address the loss of efficiency. Our simulation results indicat…

  PublisherOA PDFDOI

Recent grants

• Cutting Edge Survival Methods for Epidemiological Data

  NIH · $1.3M · 2018–2023

• High-Dimensional Inference beyond Linear Models

  NSF · $200k · 2019–2022

• Statistical Methods for Alzheimer's Research

  NIH · $1.7M · 2022–2027

Frequent coauthors

• Yue Wang

  85 shared

• Joshua D. Stein

  University of Michigan–Ann Arbor

  61 shared

• Norman E. Breslow

  University of Washington

  52 shared

• Daniel M. Green

  St. Jude Children's Research Hospital

  51 shared

• David Childers

  39 shared

• Shahzad I. Mian

  37 shared

• John A. Kalapurakal

  Northwestern Medicine

  26 shared

• Giulio J. D’Angio

  University of Pennsylvania

  26 shared

Awards & honors

• Professors Nan and Gillen Receive $1.8M Grant to Study Stati…

Similar researchers at University of California, Irvine

• Simin Liuh-187
• Wendy Cozenh-89
• Daniel L. Gillenh-79