Core Course Descriptions
Please Note: Course instructors and descriptions are subject to change.
Fall Core Courses
PHS 701 Applied Analytic Methods for Population Health Sciences I (Syllabus)
Instructors: Drs. Emily O'Brien and Brad Hammill
Students will get an introduction to study design, descriptive statistics, and analysis of statistical models with one or two predictor variables. Topics include: principles of study design, basic study designs, descriptive statistics, sampling, contingency tables, one- and two-way analysis of variance, simple linear regression, and analysis of covariance. Both parametric and nonparametric techniques are also explored. Core concepts are taught through team-based case studies and analysis of research datasets taken from the population health sciences literature and demonstrated in concert with PHS 703 (Introduction to SAS Programming for Population Health Sciences). Computational exercises will primarily use the SAS Statistical Computing Platform.
PHS 703 Introduction to Statistical Programming for Population Health Sciences I (Syllabus)
Instructors: Dr. Theresa Coles and Jared Dean, SAS Institute
1.5 hours, concurrent with PHS 701
Students will be introduced to statistical software packages (e.g., SAS Software System, R Statistical Computing Platform) to provide an introduction to the core ideas of programming, including data preparation, input/output, debugging, and strategies for program design. Students will learn to write code to perform descriptive, statistical, and graphical analyses, and write maintainable code, to test for correctness, and to apply basic principles of reproducibility. Programming techniques and their applications will be closely connected with the methods and examples presented in the co-requisite applied analytic methods course PHS 701. This course assumes minimal programming knowledge.
PHS 705 Topics in Population Health Sciences I (Syllabus)
Instructors: Drs. Leah Zullig, Matt Dupre, and Laura Richman
Students will gain foundational knowledge in the US healthcare system, population health sciences, and health and healthcare including an introduction to major diseases and disorders. Topics include: overall structure of the US healthcare system, insurance, Medicare, Medicaid, VA system, the ACA, mental health, health economics, and quality of care.
PHS 707 Population Health Sciences Research Methods and Study Design I (Syllabus)
Instructors: Drs. Bryce Reeve and Heather King
This is the first in a two-course sequence that gives students a strong foundation in population health research methods. The course introduces critical concepts in research methods, including varying types of validity, reliability, and causal inference. Topics include: sampling and interpretation of probability and nonprobability sampling ; an introduction to measurement theory; threats to internal validity; experimental designs; and quasi-experimental designs.
PHS 709 Professional Development I (Syllabus)
Instructors: Dr. Asheley Skinner
This multi-semester course gives students a holistic view of their career choices and how to develop the tools they’ll need to succeed professionally. Fall semester focuses on creating a strong professional presence, proper networking techniques, American employer expectations, creating and maintaining a professional digital presence, and learning how to conduct and succeed at informational interviews. Students will attend interviewing and networking events with Duke staff and faculty as well as external guests.
Spring Core Courses
PHS 702 Applied Analytic Methods for Population Health Sciences II
Instructors: Drs. Matt Maciejewski and Valerie Smith
This course is a continuation of PHS 701. Topics include: analysis of multivariable statistical models with continuous, dichotomous and survival outcomes. Topics include mixed effects models, generalized linear models (GLM), basic models for survival analysis and regression models for censored survival data, clustered data. Students will explore parametric and nonparametric and perform computational exercises using the SAS System and R Statistical Computing Platform.
PHS 704 Introduction to Statistical Programming for Population Health Sciences II
Instructors: Dr. Theresa Coles and Jared Dean, SAS Institute
1.5 hours, concurrent with PHS 702
Students will build on programming learned in PHS 703 using the SAS Software System and R Statistical Computing Platform. Students will perform descriptive, statistical, and graphical analyses, and write maintainable code, test code for correctness, and apply basic principles of reproducibility. Programming and assignments will be closely connected with the methods and examples presented in the co-requisite applied analytic methods course PHS 702.
PHS 706 Topics in Population Health Sciences II
Instructors: Drs. Hayden Bosworth and Virginia Wang
This course is a continuation of topics introduced in PHS 705 including: definition and measurement of population health; an overview of determinants of health including medical care, socioeconomic status, the physical environment and individual behavior, and their interactions; an overview of health services research, dissemination and implementation science, epidemiology, and measurement sciences.
PHS 708 Population Health Sciences Research Methods and Study Design II
Instructors: Drs. Lesley Curtis and Sudha Raman
This is the second in a two-course sequence that gives students a strong foundation in population health research methods. Topics include: qualitative and mixed methods, and advanced designs relevant to population health. The course applies foundational design information to methods unique to population health, including pragmatic trials, administrative claims data, and electronic medical record data. The course culminates in the development of a strong research question for a literature review, using the methods learned to critique research on a topic of the student’s choosing.
PHS 710 Professional Development II
Instructors: Dr. Asheley Skinner
This course is a continuation of PHS 709 and teaches project and team management. This course will give the student a holistic view of career choices and development and the tools they will need to succeed as professionals in the world of work.
Students can choose electives offered through DPHS or courses housed in other departments. Electives from outside of DPHS should be chosen in consultation with your mentor, and are approved by the Director of Graduate Studies.
Economic Evaluation in Health Care
Instructor: Dr. Shelby Reed
Health technology assessments that include robust economic evaluations are routinely conducted in jurisdictions around the world to inform health policy and coverage decisions. Stakeholders throughout health care systems should understand the strengths and limitations of methods used in economic evaluations, particularly as 'value-based' models evolve.
This course will provide an introduction to the principles and methods used in economic evaluations of diagnostic tests and therapeutic interventions. Methods will include decision analysis, evidence synthesis, statistical analysis of medical resource use and cost data, and survival analysis.
Improving Population Health through Implementation Science
Instructors: Drs. Leah Zullig and Heather King
Implementation science addresses the translation of evidence-based practices, programs and policies into real world settings. This course will include didactic lectures, with case studies, applied group work, and a culminating real-world, hands-on implementation, dissemination, de-implementation, or QI science project.
Fundamentals of qualitative research implementation
Instructors: Dr. Amy Corneli and Brian Perry
This course prepares learners for serving as a research assistant on qualitative research studies. Learners will gain competency in 1) conducting qualitative research studies, with an emphasis on study coordination and interviewing skills, and 2) managing data and conducting applied thematic analysis.
Learners will have competency in 1) coordinating qualitative studies (e.g., screening, recruitment, regulatory, scheduling), 2) conducting qualitative interviews (e.g., demonstrable skills in leading in-depth interviews and focus groups), 3) managing study data, and 4) conducting qualitative analysis (e.g., demonstrable skills in analysis steps, use of software).
Pragmatic Health Policy Research
Instructor: Dr. Aaron McKethan|
This course covers the foundational principles of health policy and policy science, and continues on to consider practical examples of research being used to change policy at various levels.
This course bridges the divide between analysis/methods courses (generating evidence) and policy courses (understanding specific policy areas, process and stakeholders) to help students build foundational knowledge and focused skills in framing/communicating timely, policy-relevant evidence, applicable to many population health-related career paths.
Students will increase knowledge and mastery of theoretical and substantive foundations of pragmatic policy analysis, specific policy areas and issues (e.g. SNAP, Medicaid, opioid use disorder, infant mortality, etc).
Students will be able to clearly communicate policy-relevant information, orally and in writing.
Quality of Care and Population Health
Instructor: Dr. George Jackson
The goal of enhancing the quality of care and services provided by healthcare and community organizations is at the heart of much of the practice, evaluation, and research in population health sciences. This course focuses on: 1) defining and identifying quality goals; 2) determining measures of quality; 3) planning projects to improve quality; and 4) summarizing the impact of quality improvement efforts.
In addition to the electives offered within DPHS, students may take electives from other schools and departments across Duke. The following are examples of courses offered by departments outside Population Health. The Director of Graduate Studies will approve all outside electives.
I. Health Services Research
A. CRP 248 - Clinical Trials
II. Implementation Science
A. CLP 213 - Health Care Organization and Policy
B. CLP 214 - Population Health Management Approaches
C. CLP 215 - Health Care Operations: Perspectives for Continuous Improvement
III. Comparative Effectiveness
A. CRP 262 - Systematic Reviews and Meta Analysis
B. CRP 266 - Concepts in Comparative Effectiveness Research
IV. Cost Effectiveness
A. ECON 608D - Introduction to Econometrics
B. ECON 612 - Time Series Econometrics
C. ECON 613 - Applied Econometrics in Microeconomics
D. ECON 620 - Game Theory with Applications of Economics and other Social Sciences
E. ECON 703 - Econometrics I
F. ECON 707 - Econometrics II
G. ECON 756 - Health Economics: Supply
H. ECON 757 - Health Economics: Demand
I. GLHLTH 531 - Cost-Benefit Analysis for Health and Environmental Policy
V. Measurement Science
A. POLSCI 732 - Research Design and Qualitative Methods (M)
B. SOCIOL 699 - Qualitative Methods in Sociology
C. SOCIOL 720 - Survey Research Methods
VI. Decision Science
A. CRP 259 - Decision Sciences in Clinical Research
B. MMCI 517 - Spreadsheet Modeling and Decision Analysis
C. MMCI 540 - Managerial Analysis
A. GLHLTH 635 - Critical Readings in Environmental Epidemiology
B. GLHLTH 708 - Advanced Methods in Epidemiology
C. GLHLTH 710 - Intermediate Epidemiology
D. SOCIOL 720 - Survey Research Methods
E. UPGEN 533 - Genetic Epidemiology
A. POLSCI 733 - Advanced Regression
B. PSY 767 - Applied Correlation and Regression Analysis
C. PSY 768 - Applied Structural Equation Modeling
D. PSY 770 - Applied Multilevel Modeling
E. SOCIOL 726S - Advanced Methods of Demographic Analysis
F. STA 521L - Modern Regression and Predictive Modeling
G. STA 523L - Programming for Statistical Science
H. STA 601 - Bayesian and Modern Statistical Data Analysis
IX. Data Science
A. COMPSCI 316 - Introduction to Database Systems
B. COMPSCI 516 - Database Systems
C. SOCIOL 728 - Advanced Methods: Introduction to Social Networks
D. COMPSCI 570 - Artificial Intelligence
E. COMPSCI 571D - Machine Learning
F. COMPSCI 579 - Statistical Data Mining
G. MIDS 5XX - Data to Decision
H. MIDS 5XX - Data Marshaling & Management
I. MIDS 5XX - Data Visualization
J. MIDS 5XX - Data Science Seminar
K. MMCI 538 - Data, Information and Knowledge Representation
L. SOCIOL 728 - Advanced Methods: Introduction to Social Networks
M. STA 561D - Probabilistic Machine Learning
N. STA 571 - Advanced Probabilistic Machine Learning