With the advice and approval of his or her faculty adviser, the first- year student may choose approved electives offered by the Department of Behavioral Science. The intent is to provide the student an opportunity for exploration and study in an area which supplements and/or complements required course work in the first-year curriculum. Pass-fail only.
With the advice and approval of his or her faculty adviser, the second-year student may choose approved electives offered by the Department of Behavioral Science. The intent is to provide the student an opportunity for exploration and study in an area which supplements and/or complements required course work in the second-year curriculum. PASS-FAIL ONLY.
BST 230 provides students with an introduction to statistical concepts that are important for solving real-world public health problems. This course will present statistical principles and associated scientific reasoning underlying public health practice and health policy decision- making. Topics include data visualization, summary statistics, statistical inference, study design and data analysis, and strategies for articulating and evaluating claims using statistical constructs.
BST 230 provides students with an introduction to statistical concepts that are important for solving real-world public health problems. This course will present statistical principles and associated scientific reasoning underlying public health practice and health policy decision- making. Topics include data visualization, summary statistics, statistical inference, study design and data analysis, and strategies for articulating and evaluating claims using statistical constructs.
BST 635 covers basic concepts on databases with applications to public health. Students will learn how to program in SAS, the leading statistical analysis system. SAS skills include managing data, using SQL, generating descriptive statistics, visualizing data, writing reports, writing MACROs, and programming using SAS.
BST 636 covers statistical techniques for issues associated with the exploration of large public health data sets and the development of models from such data sets. The practical issues involved in analyzing large observational healthcare data will be addressed with a focus on appropriate interpretations and the effective communication of results.
BST 661 provides an introduction to common concepts and methods used in the display and analysis of time to event data. Topics include censoring, hazard rates, estimation of survival curves, regression techniques, applications to human health studies.
This course, the second in a two-semester sequence in regression modeling, covers regression models for outcomes which are not normally distributed, such as binary and count data. The course will cover the generalized linear model framework, multivariate maximum likelihood theory, logistic regression, Poisson regression, and nominal and ordinal logistic regression models, as well as approaches for building models and checking assumptions. The course will include the use of computing tools to apply these models to real data.
This course is designed to present a range of dental practice models and introduce several elements of Practice Management. Students will become acquainted with concepts such as business plans, billing, collections, and risk management, and will have the opportunity to engage in guest lectures from outside experts in a range of legal and business fields.
Advanced Concepts in Dental Public Health is a third year course designed to help students develop the perspective and sensitivities of dentists practicing in the community. It examines the external environment and various factors that influence the oral health of the community including barriers to care. Financing mechanisms and workforce issues will be discussed.
