The Master of Science in Biostatistics Program is pleased to announce the following courses that will take place in the Spring II term, beginning Monday April 2nd and ending Friday June 22nd.
BIO9001 – Applied Analysis of Healthcare Databases (3 credits) – Lecture: Thursdays 4:00 to 6:00 pm; Lab: Thursdays 6:00 to 7:00 pm; Course Director: Natalia Egorova
This course will prepare students to identify and use national and local healthcare databases in their own research. Students will evaluate published database studies, complete programming exercises with SAS statistical software and hands-on access to a large database, and prepare a proposal for analyzing a specific research question using a large healthcare database.
BIO9100 – Survival Analysis (3 credits) – Lecture: Wednesdays 3:00 to 5:00 pm; Lab: Tuesdays 3:00 – 4:00 pm; Course Director: Umut Ozbek
This course describes the analysis of time-to-event data. Several concepts of censoring are introduced, as are functions used to describe survival distributions. Both parametric and nonparametric methods to describe and compare survival distributions are given. Cox regression is studied including the assumptions required, examining the validity of these assumptions, and dealing with time dependent covariates. Interval censored data are explored, as well as the analysis of multiple failures. Analyzing data sets will be required.
BIO9200 – Analysis of Longitudinal Data (3 credits) – Lecture: Mondays 4:00 – 6:00 pm; Lab: Tuesdays 4:00 – 5:00 pm; Course Director: Mayte Suarez-Farinas
The aim of this course is to provide systematic training in both the theoretical foundations and the model building strategies of linear regression models for students who have already had some data analysis experience. Modern approaches to the analysis of longitudinal data are presented. The course is organized as a two-hour lecture in which the statistical methodology for longitudinal data is discussed and a one-hour lab in which R will be used to perform analysis of actual data.
BIO9002 – Race and Causal Inference Seminar (1 Credit) – Thursdays 1:00 – 3:00 pm; Course Director: Dr. Emma Benn
In this course, we will question the operationalization of race as a “cause” when examining racial disparities in health from a statistical framework grounded in the underlying theories of causal inference. By the end of this course, students will have gained a unique set of knowledge that they can use to: 1) more critically scrutinize the traditional approaches to investigating disparities in health (not just specific to race), and 2) apply a more nuanced inferential, rather than descriptive, approach to future work in the disparities arena that will move us closer to finding efficacious interventions.