Course Offerings

Biomedical Informatics (PhD, MS)

Biomedical informatics at Vanderbilt trains future leaders through a foundation of principles and theory, mentored scientific research, and large-scale development and implementation. The interdisciplinary field is grounded in the principles of computer science, information science, cognitive science, social science, and engineering, as well as the clinical and basic biological sciences. Funding is available through a National Library of Medicine Training Grant and Department of Veterans Affairs’ Post Doctoral Fellowship in Medical Informatics & Quality Improvement.

Biostatistics (PhD, MS)

The Biostatistics graduate program emphasizes modern statistical thought and features the foundations of statistical inference, a topic of critical importance. The program aims to strike a balance between theoretical rigor, methodological proficiency, and functional aptitude. There is a strong emphasis on reproducible, validated research and how to achieve this from a statistical perspective.

Epidemiology (PhD)

Epidemiology at Vanderbilt emphasizes training in advanced quantitative methods with strong roots in logic and causal inference. We engage students immediately as active collaborators in multidiciplinary research teams, focus on analysis of observational data, and encourage scholarly productivity throughout training. The Ph.D. is based in The Institute for Medicine and Public Health with more than 127 researchers here who excel in large-scale collaborative research with basic and applied scientists worldwide focused on protecting and improving human health.

Public Health (MPH)

The Vanderbilt Master of Public Health (MPH) Program is a two-year interdisciplinary program offered through the Vanderbilt University School of Medicine and accredited by the Council on Education for Public Health.

Since 1996, the program has been training future public health professionals and research scientists to be leaders and innovators dedicated to improving public health in a program environment rich in learning, discovery, and service.

We offer three tracks: Epidemiology , Global Health , and Health Policy .

  • BMIF 6300. Foundations of Biomedical Informatics
    This introductory course examines the unique characteristics of clinical and life science data and the methods for representation and transformation of health data, information, and knowledge to improve health care. Principles of information security and confidentiality are taught, along with functional components of information systems in clinical settings and the use of databases for outcome management. Through skill modules and weekly programming exercises, the course provides an introduction to methods underlying many biomedical informatics applications, including information retrieval, medical decision making, evaluation of evidence, and knowledge representation. The historical evaluation of the field of biomedical informatics is taught concurrently, using examples of landmark systems developed by pioneers in the field. FALL. [3] Johnson, Weinberg.
  • BMIF 6310. Foundations of Bioinformatics
    This survey course introduces students to the experimental context and implementation of key algorithms in bioinformatics. The class begins with a review of basic biochemistry and molecular biology. The group will then focus on algorithms for matching and aligning biological sequences, given the context of molecular evolution. The emphasis will move from comparing sequences to the systems developed to enable high-throughput DNA sequencing, genome assembly, and gene annotation. Gene products will be the next focus as students consider the algorithms supporting proteomic mass spectrometry and protein structure inference and prediction. The informatics associated with transcriptional microarrays for genome-wide association studies will follow. Finally, the class will examine biological networks, including genetic regulatory networks, gene ontologies, and data integration. Formal training in software development is helpful but not required. Students will write and present individual projects. Undergraduates need the permission of the instructor to enroll. FALL. [3] Tabb.
  • BMIF 7311. Systems Biology
    This survey course presents the student with the historical, conceptual, and technical foundations of systems biology as it relates to biomedical research using model systems as well as human disease. SPRING. [3] Levy.
  • BMIF 6315. Methodological Foundations of Biomedical Informatics
    In this course, students will develop foundational concepts of computation and analytical thinking that are instrumental in solving challenging problems in biomedical informatics. The course will use lectures and projects directed by co-instructors and guest lecturers. SPRING. [3] D. Giuse.
  • BMIF 6321 Scientific Communication
    The course will enhance students’ skills in written and oral scientific communication. An introductory segment covers categories of scientific writing, the peer review process, and ethical issues in research communication. Through a two-semester sequence, it provides direct, hands-on experience in writing papers, abstracts, and grant proposals; critiquing and copy editing; and preparing and giving presentations for scientific meetings. FALL, SPRING. [1-1] Aronsky, Miller.
  • BMIF 6322 Scientific Communication
    The course will enhance students’ skills in written and oral scientific communication. An introductory segment covers categories of scientific writing, the peer review process, and ethical issues in research communication. Through a two-semester sequence, it provides direct, hands-on experience in writing papers, abstracts, and grant proposals; critiquing and copy editing; and preparing and giving presentations for scientific meetings. FALL, SPRING. [1-1] Aronsky, Miller.
  • BMIF 6331. Student Journal Club and Research Colloquium
    The class meets weekly and is a seminar course that involves two revolving formats: journal club presentations and student research in progress presentations. For Biomedical Informatics graduate students only, usually taken in the second year of the program. Fall [1] Denny, Jerome.
  • BMIF 6332. Student Journal Club and Research Colloquium
    The class meets weekly and is a seminar course that involves two revolving formats: journal club presentations and student research in progress presentations. For Biomedical Informatics graduate students only, usually taken in the second year of the program. Spring [1] Denny, Jerome.
  • BMIF 6341 Research Rotation in Biomedical Informatics
    Students will perform research under the direction of a faculty adviser. FALL. [1-1] Staff.
  • BMIF 6342. Research Rotation in Biomedical Informatics
    Students will perform research under the direction of a faculty adviser. SPRING. [1-1] Staff.
  • BMIF 7320. Healthcare System and Informatics
    The purpose of this course is for students to understand the organizational world in which they will spend most of their professional lives. A better understanding will lead to strategies to build partnerships with physicians, researchers, hospitals, and academic organizations. In turn, better understanding will lead to working more closely as a team in planning future directions and implementing technological programs and changes. This course provides an overview of theoretical concepts as well as the practical tools for the student to understand and work effectively with two major topic areas: (1) understanding the health care environment; and (2) understanding organizational informatics, including the implementation of informatics systems and the concepts of behavioral change management. Prerequisite: BMIF 300 is a required prerequisite to this course. SPRING. [3] Lorenzi.
  • BMIF 7330. Medical Artificial Intelligence
    This course builds on the material covered in Methodological Foundations of Biomedical Informatics (BMIF 315) by introducing several additional machine learning concepts and algorithms with a focus on biomedical decision making and discovery. Even though biomedical applications and examples will be discussed, the methods have broad applicability in science and engineering. The following topics will be covered in this course (may be expanded or modified based on the background of the class participants): decision support systems, natural language processing and text mining, Bayesian networks, neural networks, decision trees, feature selection, SVM regression and unsupervised SVMs, hidden Markov models, Bayesian network learning, and causal discovery using Bayesian networks. Prerequisite: for Biomedical Informatics students, BMIF 315; for non-Biomedical Informatics students, a course in data structures or algorithm design and analysis, the ability to program in MATLAB version 6 or later, and basic concepts of machine learning and fundamental mathematical concepts needed in machine learning at the level covered in BMIF 315. SPRING. [3] Mani.
  • BMIF 7340. Clinical Information Systems and Databases
    course builds on material covered in Methodological Foundations of Biomedical Informatics (BMIF 315) by introducing and developing concepts in distributed systems and network computing: OSI stack, protocols, TCP/IP, Sockets, and DNS; clinical database concepts: synchronization, concurrency, deadlock, full-text databases; distributed database services, including high-availability techniques; and architectural considerations in the design of clinical information systems. The VUMC clinical database architecture is used as a case study. Prerequisite: for Biomedical Informatics students, BMIF 315 or permission of instructor; for non-Biomedical Informatics students, coding ability in some standard procedural or object-oriented computer language, preferably PERL. FALL. [3] D. Giuse.
  • BMIF 6360. Graduate Seminar on Biomedical Informatics Algorithms
    Graduate-level topics in intermediate or advanced algorithms, data structures, and knowledge representations for biomedical informatics that are not covered in the M.S./Ph.D. core courses. Note: covered topics will be highly dependent on faculty and student interests and will change from year to year to reflect research advances and interests. Students must obtain instructor permission to enter the class. [1-3] (Not currently offered)
  • BMIF 7999 Master’s Thesis Research
    Section Content Here
  • BMIF 7370. Evaluation Methods in Biomedical Informatics
    Students are introduced to health information technology evaluation, with exposure to study design, including sampling, appropriate use of controls; data collection, including human subjects research considerations; analysis, including testing for statistical significance, definitions of sensitivity and specificity, ROC plots; and reporting of results. Quantitative and qualitative methods will be covered, as well as methods and issues specific to health care settings. FALL. [3] Gadd, Peterson, Aronsky.
  • BMIF 8999. Non-Candidate Research
    Research prior to entry into candidacy (completion of qualifying examination) and for special non-degree students. [Variable credit: 0-12]
  • BMIF 7380. Data Privacy in Biomedicine
    This course introduces students to concepts for evaluating and constructing technologies that protect personal privacy in data collected for primary care and biomedical research. Material in this course touches on topics in biomedical knowledge modeling, data mining, policy design, and law. Prerequisite: students are expected to be proficient in writing basic software programs, although no specific programming language is required. SPRING. [3] Malin.
  • BMIF 7391. Special Topics Seminar in Biomedical Informatics
    This course is designed for faculty to offer small groups of students a study course on a topic of mutual interest and concern in the faculty member’s area of expertise.
  • BMIF 7395. Directed Research/Independent Study
    Students will work under close supervision of a specific faculty member on an ongoing research problem. Depending on the specific project, students will learn aspects of study design, research methods, data collection and analysis, research manuscript writing, and human factors engineering. SPRING/FALL.[1-3] Staff.
  • BMIF 9999. Ph.D
    Dissertation Research.

  • BIOS 6301. Introduction to Statistical Computing
    This course is designed for students who seek to develop skills in statistical computing.

    Students will learn how to use R and STATA for data management, database querying, reporting generating, data presentation, and data tabulation and summarization. Topics will include organization and documentation of data, input and export of data sets, methods of cleaning data, tabulation and graphing of data, programming capabilities, and an introduction to simulations and bootstrapping. Students will also be introduced to LaTeX and Sweave for report writing. Students will also be briefly introduced to SAS and SQL programming. FALL. [2] Staff.
  • BIOS 6311. Principles of Modern Biostatistics
    This is the first in a two-course series designed for students who seek to develop skills in modern biostatistical reasoning and data analysis. Students learn the statistical principles that govern the analysis of data in the health sciences and biomedical research. Traditional probabilistic concepts and modern computational techniques will be integrated with applied examples from biomedical and health sciences. Statistical computing uses software packages STATA and R; prior familiarity with these packages is helpful but not required. Topics include: types of data, tabulation of data, methods of exploring and presenting data, graphing techniques (boxplots, q-q plots, histograms), indirect and direct standardization of rates, axioms of probability, probability distributions and their moments, properties of estimators, the Law of Large numbers, the Central Limit Theorem, theory of confidence intervals and hypothesis testing (one sample and two sample problems), paradigms of statistical inference (Frequentist, Bayesian, Likelihood), introduction to non-parametric techniques, bootstrapping and simulation, sample size calculations and basic study design issues. One hour lab required; Students are required to take 311L concurrently. Prerequisite: Calculus I. FALL. [3] Staff.
  • BIOS 6311L. Principles of Modern Biostatistics Lab
    This is a discussion section/lab for Principles of Modern Biostatistics. Students will review relevant theory and work on applications as a group. Computing solutions and extensions will be emphasized. Students are required to take 311 concurrently. FALL. [1] Staff.
  • BIOS 6312. Modern Regression Analysis
    This is the second in a two-course series designed for students who seek to develop skills in modern biostatistical reasoning and data analysis. Students learn modern regression analysis and modeling building techniques from an applied perspective. Theoretical principles will be demonstrated with real-world examples from biomedical studies. This course requires substantial statistical computing in software packages STATA and R; familiarity with at least one of these packages is required. The course covers regression modeling for continuous outcomes, including simple linear regression, multiple linear regression, and analysis of variance with one-way, two-way, three-way, and analysis of covariance models. This is a brief introduction to models for binary outcomes (logistic models), ordinal outcomes (proportional odds models), count outcomes (poisson/negative binomial models), and time to event outcomes (Kaplan-Meier curves, Cox proportional hazard modeling). Incorporated into the presentation of these models are subtopic topics such as regression diagnostics, nonparametric regression, splines, data reduction techniques, model validation, parametric bootstrapping, and a very brief introduction to methods for handling missing data. One hour lab required. Students are required to take 312L concurrently. Prerequisite: Biostatistics 311 or equivalent; familiarity with STATA and R software packages. SPRING. [3] Staff.
  • BIOS 6312L. Modern Regression Analysis Lab
    This is a discussion section/lab for Modern Regression Analysis. Students will review relevant theory and work on applications as a group. Computing solutions and extensions will be emphasized. Students are required to take 312 concurrently. SPRING. [1]
  • BIOS 6321. Clinical Trials and Experimental Design
    This course covers the statistical aspects of study designs, monitoring and analysis. Emphasis is on studies of human subjects, i.e. clinical trials. Topics include: principles of measurement, selection of endpoints, bias, masking, randomization and balance, blocking, study designs, sample size projections, study conduct, interim monitoring of accumulating results, flexible and adaptive designs, sequential analysis, analysis principles, adjustment techniques, compliance, data and safety monitoring boards (DSMB), Institutional Review Boards (IRB), the ethics of animal and human subject experimentation, history of clinical trials, and the Belmont report.
  • BIOS 7323. Applied Survival Analysis
    This course provides an applied introduction to methods for time-to-event data with censoring mechanisms. Topics include: life tables, nonparametric approaches (e.g., Kaplan-Meir, log-rank), semi-parametric approaches (e.g., Cox model), parametric approaches (e.g., Weibull, gamma, frailty) competing Risks (introduce Poisson regression as connection to Cox model), and time-dependent covariates. Focus is on fitting the models and the relevance of those models for the biomedical application. [3] Chen
  • BIOS 7323L. Applied Survival Analysis Lab
    This is a discussion section/lab for Applied Survival Analysis. Students will review relevant theory and work on applications as a group. Computing solutions and extensions will be emphasized. Students are required to take 323 concurrently. [1] Fall
  • BIOS 7330. Regression Modeling Strategies
    The course presents strategies for, and a survey of current thinking on, building predictive models. Multivariable predictive modeling for a single response variable: using regression splines to relax linearity assumptions, perils of variable selection and over-fitting, where to spend degrees of freedom, shrinkage, imputation of missing data, data reduction, and interaction surfaces. Methods for graphically understanding models (e.g., using nomograms) and using resampling to estimate a model’s likely performance on new data. Statistical methods related to binary logistic models and ordinal logistic and survival models will be covered. Students will develop, validate, and graphically describe multivariable regression models. Prerequisite: BIOS 311 and 312 or permission [3] Spring. Harrell.
  • BIOS 6341. Fundamentals of Probability
    The first in a two-course series (341-342), Fundamentals of Probability introduces and explores the probabilistic framework underling statistical theory. Students learn probability theory -- the formal language of uncertainty -- and its application to everyday statistical concepts and analysis methods. Students will validate analytical solutions and explore limit theorems using R software. This course covers probability axioms, probability and sample space, events and random variables, transformation of random variables, probability inequalities, independence, discrete and continuous distributions, expectations and variances, conditional expectation, moment generating functions, random vectors, convergence concepts (in probability, in law, almost surely), Central Limit Theorem, weak and strong Law of Large Numbers, extreme value distributions, order statistics.
  • BIOS 6341L. Fundamentals of Probability Lab
    This is a discussion section/lab for Fundamentals of Probability. Students will review relevant theory and work on applications as a group. Computing solutions and extensions will be emphasized. Students are required to take 341 concurrently.
  • BIOS 6342. Contemporary Statistical Inference
    The second in a two-course series (341-342), Contemporary Statistical Inference introduces and explores the fundamental inferential framework for parameter estimation, testing hypotheses, and interval estimation. Students learn classical methods of inference (hypothesis testing), and modes of inference (Frequentist, Bayesian and Likelihood approaches) and their surrounding controversies. Topics include: delta method, sufficiency, minimal sufficiency, exponential family, ancillarity, completeness, conditionality principle, Fisher’s Information, Cramer-Rao inequality, hypothesis testing (likelihood ratios test, most powerful test, optimality, Neyman-Pearson lemma, inversion of test statistics), Likelihood principle, Law of Likelihood, Bayesian posterior estimation, Interval estimation (confidence intervals, support intervals, credible intervals), basic asymptotic and large sample theory, maximum likelihood estimation, resampling techniques (e.g., bootstrap).
  • BIOS 6342L. Contemporary Statistical Inference Lab
    This is a discussion section/lab for Contemporary Statistical Inference. Students will review relevant theory and work on applications as a group. Computing solutions and extensions will be emphasized. Students are required to take 342 concurrently.
  • BIOS 7345. Advanced Regression Analysis I (Linear and General Linear Models)
    Students are exposed to a theoretical framework for linear and generalized models. First half of the semester covers linear models: multivariate normal theory, least squares estimation, limiting chi-square and F-distributions, sum of squares (partial, sequential) and expected sum of squares, weighted least squares, orthogonality, Analysis of Variance (ANOVA). Second half of the semester focuses on generalized linear models: binomial, Poisson, multinomial errors, introduction to categorical data analysis, conditional likelihoods, quasi-likelihoods, model checking, matched pair designs. [3] Saville.
  • BIOS 7345L. Advanced Regression Analysis I Lab
    This is a discussion section/lab for Advanced Regression Analysis. Students will review relevant theory and work on applications as a group. Computing solutions and extension will be emphasized. Students are required to take 345 concurrently. [1] Fall.
  • BIOS 7346. Advanced Regression Analysis II (General Linear & Longitudinal Models)
    Second in a yearlong series, students are exposed to a theoretical framework for generalized linear and longitudinal models. Covers classic repeated measures models, random effect models, generalized estimating equations (GEEs), Hierarchical models, and transitional models for binary data, marginal vs. mixed effects models, model fitting, model checking, clustering, and implication for study design. Also includesdiscussion of missing data techniques, Bayesian and Likelihood methods for GLMs, and various fitting algorithms such as maximum likelihood and generalized least squares. Prerequisite: BIOS 345 [3] Spring. Schildcrout.
  • BIOS 7346L. Advanced Regression Analysis II Lab
    This is a discussion lab for Advanced Regression Analysis II. Students will review relevant theory and work on applications as a group. Computing solutions and extensions will be emphasized. Students are required to take BIOS 346 concurrently. FALL. [1] Schildcrout.
  • BIOS 7351. Statistical Collaboration in Health Sciences I
    First course of two on collaboration in statistical science. Students are exposed to a variety of problems that arise in collaborative arrangements. The course’s goal is to sharpen students’ consulting skills while exposing them to the application of advanced statistical techniques in routine health science applications. The importance of understanding and learning the science underlying collaborations will be emphasized. Students will role-play with real investigators, discuss real consulting projects that have gone awry, and face real-life problems such as opaque scientific direction poor scientific formulation, lack of time, and ill-formulated messy data. Students will engage in several consulting projects that will involve the use of a wide range of biostatistics methods from design to analysis. Course content will also make use of departmental clinics that are run concurrently. [3] Davidson.
  • BIOS 7352. Statistical Collaboration in Health Sciences II
    Second course of a yearlong sequence in collaboration in statistical science. Students are exposed to a variety of problems that arise in collaborative arrangements. The course’s goal is to sharpen students’ consulting skills while exposing them to the application of advanced statistical techniques in routine health science applications. The importance of understanding and learning the science underlying collaborations will be emphasized. Students will role-play with real investigators, discuss real consulting projects that have gone awry, and face real-life problems such as opaque scientific direction, poor scientific formulation, lack of time, and ill-formulated messy data. Students will engage in several consulting projects that will involve the use of a wide range of biostatistics methods from design to analysis. Course content will also make use of departmental clinics that are run concurrently. Prerequisite: BIOS 351 FALL. [3] Davidson.
  • BIOS 7361. Advanced Concepts in Probability and Real Analysis for Biostatisticians
    To include characteristic functions, modes of converge, uniform integrability, Brownian motion, classical limit theorems, Lp spaces, projections, sigma-algebras and RVs, martingales, random walks, Markov chains, probabilistic asymptotics. Emphasis on measure theory is minimal. Concepts are illustrated in biomedical applications whenever possible. [3]
  • BIOS 7362. Advanced Statistical Inference
    This course is an in-depth examination of modern inferential tools. Topics include High-order asymptotics, Edgeworth expansions, nonparametric statistics, quasi-likelihood and estimating equations theory, multivariate classification methods, re-sampling techniques, statistical learning, methods and tehory of high-dimensional data, estimation-maximization (EM) algorithms, and Gibbs sampling. Concepts are illustrated in biomedical applications whenever possible. SPRING. [3] Li.
  • BIOS 8366. Advanced Statistical Computing
    Course covers numerical optimization, Markov Chain Monte Carlo (MCMC) estimation-maximization (EM), algorithms, Gaussian processes, Hamiltonian Monte Carlo, and data augmentation algorithms with applications for model fitting and techniques for dealing with missing data. Prerequisite: BIOS 301 or permission of instructor. FALL. [3] Fonnesbeck.
  • BIOS 7999. Master’s Thesis Research
    Master’s Thesis Research
  • BIOS 8372. Bayesian Methods
    This course covers the methodology and rationale for Bayesian methods and their applications. Statistical topics include the historical development of Bayesian method such as hierarchical models, Markov Chain Monte Carlo (MCMC) and related sampling methods, specification of priors, sensitivity analysis, model specification and selection. This course features applications of Bayesian methods to biomedical research. FALL [3] Choi.
  • BIOS 8999. Non-Candidate Research
    Section Content Here
  • BIOS 7393. Independent Study in Biostatistics
    Designed to allow the student to explore and/or master advanced or specialized topics in Biostatistics under the guidance of faculty with relevant expertise. May be repeated.
  • BIOS 9999. Ph.D
    Dissertation Research.

 

  • EPID 8301. Introduction to Statistical Computing and Programming Workshop
    This course is designed for students who seek to develop skills in statistical computing. Students will learn how to use R and STATA for data management, database querying, reporting generating, data presentation, and data tabulation and summarization. Topics include: organization and documentation of data, input and export of data sets; methods of cleaning data; tabulation and graphing of data; programming capabilities; and an introduction to simulations and bootstrapping. Students will also be introduced to LATEX and SWEAVE for report writing. Students will also be briefly introduced to SAS. [2]
  • EPID 8310. Causal Inference
    This course will concentrate on conceptually grasping tools of logic and critical thinking as they apply to epidemiologic research. Our emphasis will be on rigorous definition of a causal effect and the minimal conditions necessary to consistently estimate such effects. In a small group format, we will examine case studies and anchor our discussions in readings from philosophy of science, logic, and probability. We will cover examples of valid and fallacious arguments, probability calculus, probabilistic fallacies, applications of Bayes theorem, the frequentist and Bayesian perspective, counterfactual logic, introduction of directed acyclicgraphs (DAG), and interpretation of p-values and confidence intervals in epidemiologic research. [3]
  • EPID 8311. Epidemiologic Theory and Methods I
    This is the first of a two-course series on advanced epidemiologic concepts and methods that includes measures of disease frequency, measures of effect, descriptive epidemiology, study designs, bias, misclassification and effect measure modification, and ethics in epidemiologic research. A case-based approach will engage students in demonstrating concepts using actual research data and in critical appraisal of case studies and publications that feature strong and weak examples. [4]
  • EPID 8312. Epidemiologic Theory and Methods II
    This second in a two-course series provides an in-depth treatment of concepts and skills in epidemiologic research, including problem conceptualization, study design, data analysis and interpretation. Includes emphasis on how to design studies to best measure etiologic effects and includes advanced discussion of confounding, interaction, and missing data. A continued case-based approach will engage students in demonstrating concepts and methods using the students’ own data. Prerequisite: 311: Epidemiologic Theory and Methods I. [4]
  • EPID 8315. Scientific Writing I
    Scientific Writing I. Participatory course in which students develop skills in presenting research results in manuscripts, abstracts, and posters. Students work in small groups to write and critique published and unpublished manuscripts, with a focus on understanding the essential components of a scientific manuscript or presentation, as well as the process of publishing in the peer-reviewed literature and managing reviewer and editor comments and requests. [1]
  • EPID 8321. Applied Epidemiologic Methods in Regression I: Binary Data
    Applied Epidemiologic Methods in Regression I: Binary Data. Concepts and applications, including logistic regression, binomial regression, ordinal regression, multinomial regression, quantile regression, model building strategy, additive and multiplicative interaction, clustered and longitudinal data, and graphical exploration. Includes computer-based experience with real data, and an emphasis on logistic regression.
  • EPID 3823. Epidemiolgic Methods: Design and Analysis with Time-to-Event Data
    Epidemiolgic Methods: Design and Analysis with Time-to-Event Data. Concepts and applications in survival analysis and analysis of incidence rates, including truncation and censoring, life tables, nonparametric approaches (e.g. Kaplan-Meier, log-rank), semi-parametric approaches (e.g. Cox models, proportional hazards regression), parametric approaches (e.g. Weibull, gamma regression) accommodating time-dependent exposures, Poisson regression, sensitivity analysis, bootstrapping, and multiple imputation. [4]
  • EPID 8325. Scientific Writing II - Proposal Development in Epidemiology
    Scientific Writing II - Proposal Development in Epidemiology. Participatory course in which each student develops a high quality, detailed research proposal suitable for submission to NIH or AHRQ that includes both a technical proposal and a draft budget justification. Includes lecture, in-class exercises and group processes. SPRING.
  • EPID 8331. Seminar in Quantitative Methods and Measurement
    Concepts and application of cross-cutting tools used for unique and/or specialized types of measurement and instrument development for areas such as physical activity, clinical laboratory tests, and imaging studies. May be repeated. [2]
  • EPID 8332. Advanced Methods for Epidemiology
    These methods electives will be taught in modular format, most often with three modules on related methods topics, which will vary annually. Students will explore methodological issues in epidemiology like measurement error, missing data, intermediate variables, complex study designs, meta-analysis, splines, propensity scores, simulation. Exercises with provided datasets and the student’s own data will be included. May be repeated. [1-3]
  • EPID 8333. Analytic Techniques for Genetic Epidemiology
    This course will take an example-based approach to provide students with the skills necessary to conduct statistical association analysis of genetic data from human populations for genetic epidemiology studies. Topics will include quality control, statistical methods for association testing, common study design issues, future directions of genetic epidemiology and advanced topics. HGEN 330, HGEN 340, MP&B 341 recommended.
  • EPID 8340. Content Area Intensives
    These intensives are offered on a rotating basis and taught by faculty with research expertise in the content area of focus. Areas of epidemiology may include cancer, cardiovascular disease, child health, chronic disease/diabetes, genetics, global health, health care, infectious disease, nutrition, pharmacoepidemiology, reproductive, and social. May be repeated. [1-3]
  • EPID 8370. Current Topics in Research
    Students attend weekly presentations selecting from the Vanderbilt Epidemiology Center Seminar Series, Biostatistics Clinic, clinical grand rounds on topics related to content area interests, and other relevant seminars. Students will convene with faculty to reflect on and critique components of research presentations relevant to the students’ interests and to the contemporaneous topics being covered in the core epidemiology curriculum. Course assignments will focus on critical appraisal of a methodologic challenge identified in a seminar setting that has immediate relevance to the student’s own research. May be repeated. [1]
  • EPID 8371. Special Topics Seminar in Epidemiology
    Faculty offer small groups of students a study course on a topic of mutual interest and concern in the faculty member’s area of expertise. May be repeated with topic change. [1-3]
  • EPID 8372. Advanced Readings in Epidemiology
    Additional readings in specialized epidemiologic topics will be explored in depth under the guidance of a faculty member. May be repeated. [1-3]
  • EPID 8373. Independent Study in Epidemiology
    Designed to allow the student an opportunity to master advanced skills in epidemiology while pursuing special projects under individual members of the faculty in their areas of expertise. May be repeated. [1-3]
  • EPID 8374. Advanced Readings in Epidemiologic Context, Thought, and History
    Reading and discussion of seminal literature in the history of epidemiology as well as contemporary literature that provides social and cultural context for the development of the field, challenges to the application of epidemiologic findings, consideration of roles and history of public health advocacy, and exploration of topics like social justice and research ethics through the lens of fiction, nonfiction, and scientific literature. A core reading will be selected to launch each semester and students will work as a group to select the balance of the readings for the semester from a recommended source list. Discussions will be facilitated by faculty and students including guest lecturers. Minimum of masters training in quantitative discipline and research experience in epidemiology or related field is required; other graduate students with permission of the instructor.
  • EPID 8999. Non-Candidate Research
    Research prior to entry into candidacy (completion of qualifying examination) and for special non-degree students. [Variable credit: 0-12]
  • EPID 9999. Ph.D
    Dissertation Research.​

 

  • PUBH 5501. Epidemiology I
This introduction to epidemiology focuses on measures of disease frequency and association, observational study design, and diagnostic and screening tests. The course reviews the use of these tools and the role of epidemiology in measuring disease in populations, estimating risks, and influencing public policy. Study designs reviewed include cross sectional, ecologic, case-control, and cohort studies.

 

  • PUBH. 5502. Biostatistics I
This course addresses basic concepts and methods of biostatistics, including data description and exploratory data analysis, study design and sample size calculations, probability, sampling distributions, estimation, confidence intervals, hypothesis testing, nonparametric tests, analysis of continuous, categorical, and survival data, data analysis for cohort and case-control studies, relative risk and odds ratio estimation, and introduction to linear and logistic regression.

 

  • PUBH 5508. Epidemiology II
Required for students in the Epidemiology track of the M.P.H. Program, this course addresses the design of non-randomized studies and factors that are important in design selection. This includes the design of cohort studies, prospective and retrospective cohort studies, assembly and follow-up of the cohort, exposure measurement, outcome ascertainment, confounders, effect modification, calculation of measures of occurrence and effect, summary of multivariate statistical analyses for cohort studies; the case-control study, conditions necessary for validity of the case-control study, selection of controls, sources of bias in case-control studies, and multivariate analysis; as well as the ecological study, including when to use and when to avoid. The course includes didactic lectures and critical reading of important epidemiologic studies from the current medical literature. Prerequisite: Epidemiology I, Biostatistics II, Clinical Trials, or approval of instructor.

 

  • PUBH 5509. Biostatistics II
Required for students in the Epidemiology track of the M.P.H. Program, this course addresses modern multivariate analyses based on the concept of generalized linear models. This includes linear, logistic, and Poisson regression, survival analysis, fixed effects analysis of variance, and repeated measures analysis of variance. The course emphasizes underlying similarity of these methods, how to choose the right method for specific problems, common aspects of model construction, and the testing of model assumptions through influence and residual analyses. Prerequisite: Biostatistics I or consent of the instructor.
 
  • PUBH 5512. Decision Analysis in Medicine and Public Health
This course provides an overview of qualitative and quantitative decision making with a dominant focus on quantitative techniques, using clinical and economic endpoints and their role in clinical strategies of care and health policy. Topics include: cognitive heuristics, Bayes' theorem, ROC analysis, the study of diagnostic tests, meta-analysis, health states and utility measurement using expected value decision making, decision tree analysis, Markov processes and network simulation modeling, quantitative management of uncertainty, cost theory and accounting, cost-effectiveness and cost-utility analysis.
 
  • PUBH 5514. Social & Behavioral Science for Public Health
The course will address two core areas in health behavior research: (1) the measurement of knowledge, attributes, attitudes, and behaviors that are relevant to health behavior research, with a focus on scale development and (2) the dispositional and situational variables that underlie current theories of behavior and behavior change, with current applications.

 

  • PUBH 5516. Environmental Health
This course will review the three key public health functions of assessment, policy development, and assurance in relationship to environmental health issues. Topics covered will include public health surveillance activities including bioterrorism issues, food safety, air pollution, and genetics and public health. Students will learn where to obtain publicly available population data on health-related events from a variety of surveillance activities and special surveys.
 
  • PUBH 5517. Grant Writing and Scientific Communication
This course provides a foundation in grant writing for the early career scientist. Specific topics include funding sources and types of awards, research mentorship, constructing the research plan, ethics and human subjects considerations, institutional resources to support the application process, and grantsmanship. Students will speak with successful awardees, learn how grants are reviewed and scored, and participate in a mock scientific study section. Spring.
 
  • PUBH 5518. Research Ethics
Presents issues in the responsible conduct of research, including ethics, data management, research fraud, academic misconduct, and conflict of interest. The course covers federal and institutional guidelines regarding research in human and animal subjects. Topics include vulnerable populations in research, confidentiality, and the Institutional Review Board (IRB).
 
  • PUBH 5520. Health Policy Seminar
This course is designed to complement and build upon PUBH 5537: Health Services Administration: Health Care Systems. Students will examine more deeply topics related to the delivery and financing of health care. Enrollment is limited to students in the Health Policy track of the MPH Program.
 
  • PUBH 5526. Global Health Project Development
This course focuses on development of the individual student's practicum and thesis including the identification of a key global health question and design of a suitable project to address the question. Each student will complete a relevant skill-process activity, a draft of his/her practicum agreement, and a project development concept paper. Enrollment is limited to students in the M.P.H. Program.
 
  • PUBH 5527. MPH Thesis Proposal Development
This course focuses on development of the individual student's research protocol. Each student will present the background, methods, and limitations of their proposed research design in class, and complete the research protocol for the M.P.H. master's thesis. Enrollment is limited to students in the Epidemiology track of the M.P.H. Program.
 
  • PUBH 5536. Public Health Practicum
Required as part of the M.P.H. Program, the public health practicum is intended to give students the opportunity to develop practical skills and competencies in public health practice settings.
 
  • PUBH 5537. Health Services Administration: Health Care Systems
This course provides an overview of the organization, financing, and delivery of health care. It addresses the complex inter-relationships among key stakeholders in the industry, how this structure has evolved over time, and how these system-wide challenges are likely to affect health care policy in the future.
 
  • PUBH 5538. Health Services Administration: Program and Policy Evaluation
This course addresses the evaluation of changes in the health care delivery system, either through programs specifically implemented to achieve such changes or through changes in health care delivery/financing policies. The primary designs--before/after, concurrent/retrospective control, interrupted time-series--and their strengths and limitations. The course includes didactic lectures and small group critical reading/presentation of current program/policy evaluations published in leading medical journals. Prerequisite: Epidemiology II, Biostatistics II, or approval of instructor.
 
  • PUBH 5539. Health Services Evaluation: Public Health Surveillance Systems
This course will provide an overview of public health surveillance as a lens to public health practice, in terms of how public health programs are organized, financed, and operated and what surveillance data are available to inform specific programs. Public health surveillance is the ongoing process that public health agencies use to collect, manage, analyze, interpret and disseminate this information. We will review basic approaches to public health surveillance, including disease reporting regulations and notifiable diseases, surveillance for infectious diseases, chronic diseases, and adverse events, uses of surveillance data, and how surveillance data can inform public health program, policy, and practice.
 
  • PUBH 5540. Health Services Administration: Leadership and Management in Global Health
This course introduces students to principles of management and leadership of global health programs and organizations in complex and challenging environments. Students will explore diverse health systems, organizational behavior, health policy, program design, and core management techniques.
 
  • PUBH 5541. Foundational Skills in Global Health
This course introduces students to core research, field tools, assessment and implementation techniques, and evaluation methodologies commonly used in the field of global health. Students explore theories and practices used to analyze issues and intervene in global health and they examine determinants of global health and development from an interdisciplinary vantage point. Health and developmental issues across nations and cultures that require collective, partnership-based action are highlighted. The course is taught by an interdisciplinary team of faculty members using didactic, interactive and practical elements of instruction.
 
  • PUBH 5542. Foundations of Global Health
This course introduces students to key topics, concepts and methods in global health, examining determinants of complex issues and multi-dimensional approaches and interventions with a particular emphasis on low-resource settings. Taught by an interdisciplinary team of faculty members, this course uses didactic, interactive and practical elements of instruction to address international and cross-cultural health and developmental issues. At the conclusion of the course, students should be able to discuss major topics in global health and design suitable projects that address global health challenges.
 
  • PUBH 5544. Ethics in Global Health

This course provides an overview of ethical issues and standards in global health, particularly with respect to ethics in international research. Its aim is to provide students in the health professions and others interested in global health with a framework in which to recognize, examine, resolve, and prevent ethical conflicts in their international work. Through readings, lectures and discussion, students will explore diverse historical and contemporary international perspectives on the concepts of ethics and health as well as formulating recommendations for prevention and resolution of ethical conflicts related to global health.
 
  • PUBH 5549. Case Studies in Tropical Diseases
This course introduces tropical diseases and parasitology in a clinical case study format with student group leadership that is facilitated by faculty with substantial front-line tropical medicine training and experience. Written case protocols will be presented by faculty members and Infectious Disease fellows/Internal Medicine residents who will lead an interactive discussion involving pathophysiology, clinical presentation, differential diagnosis, diagnosis and treatment.
 
  • PUBH 5550. Global Health Politics and Policy
Global Health Politics and Policy introduces core global health problems facing the world's populations today and examines the efforts taken to improve health at a global level. It focuses on the social and political movements of global health issues and how these forces created and shaped global health policy both in the U.S. and among the G8 nations from 2000-2011.
 
  • PUBH 5599. MPH Thesis Research
The primary objective is the completion of the M.P.H. Program's master's thesis. Each student will work independently to coordinate research activities with their thesis committee.
 
  • PUBH 7999. MPH Thesis Seminar
In this research seminar required as part of the M.P.H. Program, second-year students present the results of their master's thesis research. Each 40-minute presentation addresses the background and significance, methods, results, and public health/research implications. Presentations are scheduled through the course director on a first come, first served basis. Before presenting their work, students must obtain the approval of their thesis committee.