Course Director: Samprit Banerjee, PhD, MStat
There has been an explosion of big data in medicine and healthcare. There are four main sources of such big data – 1) administrative databases in healthcare such as electronic health records and health insurance claims, 2) biomedical imaging (e.g. MRI, CT-Scan, X-ray etc.) 3) sensors in smartphones, wearable and implantable devices and 4) genetics and genomics. It is difficult to navigate and critically assess the statistical methods and analytic tools that are needed to conduct analytics and research with such big biomedical data. This course will introduce the four above-mentioned important sources of big data in medical studies, discuss the nuances and intricacies of how such data are generated and introduce tools to navigate such databases visualize and describe them.
Course Director: Yushu Shi, PhD
This course aims to introduce some common statistical methods and computational tools for predictive modeling, specifically regression analysis. Topics covered in this course include:
- Multivariable linear regression, variable selection, and model diagnosis
- Linear regression with variable transformation
- Generalized linear models, including Logistic regression model, Poisson regression model, variations of the Poisson model for zero-inflated data, multinomial Logistic regression model, and relevant model diagnoses
- Survival analysis: censoring mechanism, log-rank test, Kaplan-Meier curve, parametric survival models, including Cox model (and Fine and Gray model for competing risks data.)
Materials in parentheses are subject to students’ background, performance of past exams, and class progress.
Course Director: Samprit Banerjee, PhD, MStat
In the last decade, biomedical and health sciences have seen an explosion of “Big Data” problems. Such problems are commonly associated with general business analytics and marketing. Many statistical and machine learning methods are required to solve such problems. This course is going to provide the basic know-how to tackle such problems and is going to teach what is statistical learning, how to assess model accuracy, supervised and unsupervised classification techniques, tree-based methods, random forests, regularized regression techniques, resampling methods, and support vector machines. The aim of this course is to enable students to identify an appropriate statistical learning algorithm for a real-world application and be able to apply the algorithm to the data using R while being cognizant of the advantages and disadvantages of the chosen algorithm.
Course Director: Kevin Kensler, ScD
3 credits
Prerequisite: Epidemiology I (PHSC 9001)
The goal of the course is to provide students with more advanced epidemiologic methods and statistical analyses appropriate for specific study designs. This course will expand students’ knowledge of epidemiologic concepts related to the design, conduct and interpretation of epidemiologic studies.
This is the culminating capstone course of all masters-level graduate education programs. It has two aims: (1) helping students to discover and develop new and effective ways of managing and working together with all the stakeholders within the healthcare field and (2) helping accelerate a student's development of the context awareness, integrative management, and industry skills that are needed to lead in a rapidly changing healthcare sector. This capstone course puts students in a new organization, one they don’t already know well, and gives them the chance to practice hitting the ground running. This culminating course provides a deeper preparation for the next stages of a student's career. The capstone project will last the entire year: the first term involves matching students with the right project, the second term has students working with their client, and the third term consists of a detailed report and final presentation in front of the client as well as faculty and fellow classmates.
Course Director: Faculty