Key Courses - MSPH in Biostatistics
Key Courses - MSPH in Biostatistics
RSPH Core Requirements
Provides the student with basic knowledge about the behavioral sciences as they are applied to public health. Content includes an overview of each discipline and current issues for students who are not enrolled in the BSHE MPH Program.
Department of Behavioral, Social, and Health Education Sciences
Prerequisite/concurrent: BIOS 500. Emphasizes the concepts and premises of the science of epidemiology. Methods of hypothesis formulation and evaluation are stressed. Techniques for quantifying the amount of disease (or other health indicator) in populations are introduced, followed by discussion of epidemiologic study designs useful for identifying etiologic factors and other relevant correlates of disease. Students gain facility with the calculation of basic epidemiologic measures of frequency, association, and impact. The concepts of random variability, bias, and effect modification are examined in detail. The use of stratified analysis, including Mantel-Haenszel techniques, is explored. Inferences from study results are discussed. Students are required to analyze and critique studies from the current medical and scientific literature.
Department of Epidemiology
Pre-requisites: GEH, GH, and GLEPI students may not enroll unless with departmental permission.
The overarching objective of GH 500 is to equip students with critical perspectives and resources that they will need as public health professionals and global citizens in our increasingly inter-connected and interdependent world. The course introduces students to: (1) fundamental cross-cutting themes that contextualize contemporary global health issues; and (2) selected health topical areas such as maternal and child health, pandemics, and non-communicable diseases. The course provides an overview of the past, present, and expected future directions of global health.
Hubert Department of Global Health
EH 500 is a survey course designed to introduce public health students to basic concepts of environmental sciences, to the methods used to study the interface of health and the environment, to the health impacts of various environmental processes and exposures, and to the public health approach to controlling or eliminating environmental health risks. To address these concepts, basic environmental health principles (exposure assessment, environmental toxicology, environmental epidemiology, risk assessment), as well as specific environmental health issues including water and air pollution, hazardous chemical/waste exposures, climate change, and environmental drivers of disease ecology, will be covered.
Gangarosa Department of Environmental Health
Required for all MPH students. Introduces students to the US health care system, both the public and private sector. Examines the structure of the health system, current topics in health care reform, the policy process, and advocacy for public health.
Department of Health Policy and Management
1 hour online module addressing 4 of the 12 CEPH required Foundational Knowledge items. The module will begin with an introduction to a "Public Health Perspective followed by the 4 items of foundational knowledge.
PUBH students will join students from health professional programs across the Woodruff Health Sciences Center to receive didactic training to perform effectively on interprofessional teams and to apply leadership and management principles to address a relevant public health issue. Interprofessional teams will compete in a health challenge competition designed to address public health and clinical issues of importance to the Atlanta community.
Biostatistics Requirements
In addition to the courses below, students must take 9 credit hours of electives to meet a total of 48 credit hours.
Prerequisites: Multivariate Calculus (Calculus III) and Linear Algebra. This course provides a mathematically sophisticated introduction to the concepts and methods of biostatistical data analysis. It aims to provide the students the skills to collaborate with investigators and statistical colleagues in the analysis of data from biomedical and public health studies and to communicate the results of statistical analyses to a broad audience. The topics include descriptive statistics; probability; detailed development of the binomial, Poisson and normal distributions and simulation of random variables from these distributions; sampling distributions; point and confidence interval estimation; simulation studies; hypothesis testing; power analysis and sample size calculations; a variety of one- and two-sample parametric and non-parametric methods for analyzing continuous or discrete data and resampling statistics. The course will also equip students with computer skills for implementing these statistical methods using standard statistical software SAS or R.
Department of Biostatistics and Bioinformatics
Prerequisites: BIOS 508 or permission of instructor. The course covers statistical methodology for the analysis of continuous outcome data, primarily from cross-sectional studies and designed experiments. We introduce the key matrix-based methods for estimation and inference based on the multiple linear regression model. Subsequently, topics include secondary hypothesis testing and restrictions, regression diagnostics, model selection, confounding and interaction, analysis of variance and covariance, and an introduction to random effects modeling. Students will also be introduced to logistic regression modeling for binary outcome data.
Department of Biostatistics and Bioinformatics
Prerequisites: Multivariate Calculus (Calculus III) and Linear Algebra or permission of instructor. Introduction to Probability, random variables, distributions, conditional distributions, expectations, moment generating functions, order statistics, and convergence concepts.
Department of Biostatistics and Bioinformatics
Prerequisites: BIOS 512 or permission of instructor. Introduces the theory of parameter estimation, interval estimation, and tests of hypotheses. In this course, we emphasize the classical "frequentist" (i.e., Neyman-Pearson-Wald) approach to inference. As time permits, we briefly explore alternative paradigms of inference such as neo-Fisherian, Bayesian, and statistical decision theory.
Department of Biostatistics and Bioinformatics
Prerequisites: BIOS 508, BIOS 509, BIOS 512, and BIOS 513 or permission of instructor. This course aims to develop basic understanding of the analysis of time-to-event data. The concepts to be introduced include survival functions, hazard rates, right censoring, interval censoring, left truncation, and competing risks. Methods of focus are Kaplan-Meier estimates, log-rank tests, Cox proportional hazards regression models, and parametric regression models. Students will learn how to implement standard survival analysis methods using R and appropriately interpret results.
Department of Biostatistics and Bioinformatics
"Prerequisites: BIOS 509 and BIOS 513 or permission of instructor. This course introduces students to modern regression techniques commonly used in analyzing public health data. Specific topics include: (1) parametric and non-parametric methods for modeling non-linear relationships (e.g., splines and generalized additive models); (2) methods for modeling longitudinal and multi-level data that account for within group correlation (e.g., mixed-effect models, generalized estimating equations); (3) Bayesian methods; and (4) shrinkage methods and bias-variance tradeoffs.
This course draws motivating examples from environmental and social epidemiology, health services research, clinical studies, and behavioral sciences. The course provides a survey of advanced regression approaches with a focus on data analysis and interpretation. Students will gain an understanding of methods that will facilitate future independent and collaborative research for modern research problems. Students will gain practical experience using the R language for statistical computing. "
Department of Biostatistics and Bioinformatics
Prerequisites: BIOS 500 & BIOS 501 or permission of instructor. This class is designed to help students master statistical programming in SAS. Students in this class will develop programming style and skills for data manipulation, report generation, simulation and graphing. This class does not directly satisfy any competencies as defined by the Department of Biostatistics and Bioinformatics, the Rollins School of Public Health or the Council on Education for Public Health (CEPH). That being said, SAS is a primary data analysis and data management software system in use worldwide, particularly in public health settings. Students who master the skills offered in this course will have a much easier time completing the work for their thesis and will find themselves more ready for a public health career with a more analytical bent.
Department of Biostatistics and Bioinformatics
Features invited speakers, departmental faculty, students, and others who discuss special topics and new research findings.