Biostatistics: Data Science Applied to Epidemiology and Biomedicine

Course 10: Biostatistics: Data Science Applied to Epidemiology and Biomedicine

I. Course Description

Biostatistics is a discipline that studies how statistical principles and methods can be applied to address problems in biomedical research. Biomedical research requires the analysis and interpretation of large amounts of data, and biostatistics provide the necessary tools and methods for these analyses. Through biostatistics, we can better understand the law of transmission and development of disease, diagnose disease and develop treatment plans. Biostatistics can help us to identify risk factors in the sick population, evaluate the effectiveness of preventive and treatment approaches, and evaluate the safety and efficacy of new drugs. Moreover, biostatistics can help us better understand the large amount of data generated by high-throughput technologies like genomics and proteomics and extract useful information from them. In conclusion, biostatistics plays a crucial role in biomedical research. It provides a reliable way to analyze and interpret the data, helping us to better understand the complexity of biological systems and thus improve human health and quality of life.

This course aims to introduce the basics of biostatistics and the application of statistical data science in public health and biomedical research. Students will learn the basic concepts and principles in biostatistics, including study design, data collection, analysis, and interpretation. The course will cover exploratory data analysis and statistical inference, including all aspects of hypothesis testing and regression modeling. The purpose of learning this course is to help students to better use the statistical learning of classification and clustering to develop higher order research in the field of biostatistics and bioinformation.

II. Professor Introduction

Hui Zhang – Tenured professor at Northwestern University

Hui Zhang Professor undergraduate course graduated from nankai university biology, master and PhD graduated from the university of Rochester statistics, the northwestern university school of medicine, northwestern university school of epidemiology and biostatistics (CEPH), northwestern university, director of brain tumor biostatistics and bioinformatics, and responsible for the mesulan cognitive neurology and Alzheimer's disease center of biostatistics and data management, in 2021. At the same time, he is a lifetime member of the International Chinese Statistical Society (ICSA) and a core member of the American Statistical Society (ASA), and is the chief editor of the famous American academic journal Journal of Statistical Computing and Simulation.

Hui Zhang Professor Professor's research interests include missing data, longitudinal discrete data analysis, survival analysis, single-molecule localization microscopy (SMLM) image analysis, computational neuroscience, and pharmacology.

III. Syllabus

1. Background and development of statistical data science
2. Applications in public health and biomedicine
3. Exploratory data analysis and summary statistics: R-based analysis
4. 4 hypothesis testing and confidence intervals
5. Linear regression: objectives, statistical concepts, estimates, and inference
6. Model comparison and evaluation
7. Time-varying; time-series regression analysis
8. Tree model, classification tree, regression tree; pruning; random forest
9. Classification and clustering; logistic regression; Kmeans and hierarchical clustering
10. 10 Survival analysis, neural networks, and clinical trial design