MPH 0300: Introduction to Biostatistics
Course Director(s): Doucette, John
This course provides an introduction to the principles underlying biostatistical methods and their application to problems in epidemiology, public health and clinical research. Students will learn about basic probability distributions, descriptive statistics, presentation of data, hypothesis testing principles, and the specific hypothesis tests and analytic methods for a variety of data types. These analytic methods will include t tests, chi-square tests, nonparametric tests, analysis of variance, correlation, regression, and basic survival analysis methods. Students will have the opportunity to apply these methods to sample data both via direct calculation and using SAS® statistical software. Each week, a one-hour laboratory session will reinforce material from lecture with additional examples and instruction in use of the SAS® software. Methods for determining sample size and power for a variety of commonly used study designs will also be presented, as will measures of the accuracy of diagnostic and screening tests. Credits: 3 | Offered: Fall
BIO 6400: Biostatistics for Biomedical Research Course Director(s)
Bagiella, Emilia; Benn, Emma
This course covers the basic tools for the collection, analysis, and presentation of data in all areas of basics, clinical and translational research. Central to these skills is assessing the impact of chance and variability on the interpretation of research findings and subsequent implications on the understanding of disease mechanisms, drug discovery and development, and applications to clinical practice. Topics covered include: general principles of study design including internal and external validity; probability and sampling distributions, theory of confidence intervals and hypothesis testing; review of methods for comparison of discrete and continuous data including one-sample and two-sample tests, correlation analysis, linear regression, sample size and power. Additionally, students will learn to apply their statistical knowledge to complex real-world challenges, while gaining introductory statistical computing proficiency in R and SAS.
BSR1026: Applied Biostatistics for Biomedical Research (3 credits)
Course Director: Dr. Emma Benn
Term: Spring
Placement Test: No. If interested, contact Dr. Benn
Prerequisites: This course assumes that students have a working knowledge of college-level algebra and strong familiarity with logarithms and exponents. It is recommended, but not mandatory, that students have taken at least 1 college-level calculus course. All students taking this course must have successfully completed a GSBS programming course (i.e., Python, R, SAS, or MATLAB) or have prior proficiency in programming as demonstrated by evidence of prior coursework in a relevant programming language on their undergraduate/graduate academic transcript. The Course Director will make the final decision as to whether a students’ prior proficiency in programming as demonstrated on the academic transcript is sufficient given the expectations of this course.
Target audience: open to all GSBS graduate students who meet the prerequisites.
Course Description: This course covers the foundational elements for the collection, analysis, and presentation of data in biomedical research. This course will cover the following topics: general principles of study design including internal and external validity; probability and sampling distributions, theory of confidence intervals and hypothesis testing; review of methods for comparison of discrete and continuous data including one-sample and two-sample tests, correlation analysis, and linear regression. Upon completion of this course, students should have introductory proficiency in statistics to apply their expertise to current biomedical challenges. This course will additionally facilitate important discourse around rigor and reproducibility and introduce students to innovative applications of biostatistics and data science to complex, real-world biomedical research ranging from building predictive algorithms for complex diseases to genetic analysis in ancestrally diverse populations.