Methods and techniques for designing, implementing, analyzing, and interpreting observational epidemiologic studies, including cohort, case-control, and cross-sectional studies.

This course provides an understanding of basic methods and tools used by epidemiologists to study the health of populations.

Introduction to epidemiologic concepts and methods: (1) Study design (randomized trials and observational studies); (2) Measures of exposure-disease association; (3) Casual inference and bias; (4) Confounding and effect modification.

Our understanding of human genomic variation and its relationship to health is expanding rapidly. This knowledge is now being translated primarily through the field of ?precision medicine? (finding the right drug for the right person at the right time). Public health, in contrast, seeks to abate the social and environmental factors that lead to disease and health disparities. This course will provide an introduction to the field of public health genomics at this interesting point in its history. Approximately one-half of the course is devoted to Genetic Epidemiology, or the science of detecting genetic risk factors for human disease. The other half of the course will cover public health genomics, including ?precision public health?, genetic screening programs, and the possibilities and pitfalls of direct to consumer marketing of genetic tests. How genomics relates to health equity will be a recurring theme of this course. This is a graduate course designed primarily for Epidemiology MPH and PhD students, and fulfills the ?Epi Of? requirement for the MPH in Epidemiology. Graduate students from other programs are very welcome.

Principles and/ methods. Strategies for disease control and prevention, including immunization. Relevance of modes of transmission of specific agents for disease spread and prevention. Public health consequences of infectious diseases at local, national, and international levels.

The course will provide an introduction to cardiovascular disease (CVD) epidemiology. It is intended to provide a detailed perspective on the well-established risk factors for CVD, as well as an introduction to emerging risk factors. Both observational studies and clinical trials will be discussed. The class will include a main focus on prevention of cardiovascular disease, and national recommendations for treatment and prevention. Several classes will incorporate discussions of new directions and current controversies in CVD. Additionally, the class will introduce students to the CVD research in the Division of Epidemiology and Community Health.

Epidemiologic aspects of cancer. Theories of carcinogenesis, patterns of incidence and mortality, site-specific risk factors. Issues of cancer control and prevention.

Nutrition/disease relationships through application of epidemiologic methods. Characterization of various exposures to food/nutrient intakes, biological basis for nutrition/disease relationships. Studies of specific chronic diseases and nutritional intake. Design/interpretation of studies using nutritional measures. prereq: [[6320 or 6330 or 6341], [Epidemiology MPH or Public Health Nutrition MPH or Epidemiology PhD student]] or instr consent

Overview of perinatal, sexual, and reproductive health surveillance, programs, services, and policies in the U.S., with an emphasis on vulnerable populations and methods to assess and interpret perinatal, sexual, and reproductive health data. prereq: Public health student or grad student or instr consent

Part of two-course sequence intended for PhD students in School of Public Health who need rigorous approach to probability/statistics/statistical inference with applications to research in public health. prereq: Background in calculus; intended for PhD students in public hlth and other hlth sci who need rigorous approach to probability/statistics and statistical inference with applications to research in public hlth

Conceptual foundations of fundamental issues in epidemiologic methodology. How/why a given method, design, or approach might help explain population health. Strengths, limits, and potential alternatives for a given approach.

Applied methodology course designed for students in the Epi PhD program. Examples and readings are aimed at clinical/biological and social/behavioral track students.

Introduction to epidemiologic concepts and methods: (1) Study design (randomized trials and observational studies); (2) Measures of exposure-disease association; (3) Casual inference and bias; (4) Confounding and effect modification.

Methods and techniques for designing, implementing, analyzing, and interpreting observational epidemiologic studies, including cohort, case-control, and cross-sectional studies.

This course will cover the fundamental concepts of exploratory data analysis and statistical inference for univariate and bivariate data, including: ? study design and sampling methods, ? descriptive and graphical summaries, ? random variables and their distributions, ? interval estimation, ? hypothesis testing, ? relevant nonparametric methods, ? simple regression/correlation, and ? introduction to multiple regression. There will be a focus on analyzing data using statistical programming software and on communicating the results in short reports. Health science examples from the research literature will be used throughout the course. prereq: [College-level algebra, health sciences grad student] or instr consent

This course will focus on the considerable burden due to infectious diseases within middle and low-income countries, as well as the underlying risk factors that lead to their emergence and spread. Students will learn about and review different measures of disease burden and health status. Different diseases of international public health significance will be reviewed, with a focus on epidemiologic research and methods used describe and analyze disease determinants. The course will also expose students to different interventions (prevention and control strategies) that have been used in both emergency situation, and to reduce the burden of more endemic diseases that significantly impact the health of populations. The scientific literature concerning specific diseases of interest will be examined and discussed in order to illustrate these principles. We recognize that it is impossible to cover all subjects in global health. Using a case-study approach, the course will instead select a variety of infectious diseases of international importance. We will focus instead on approaches to dealing with these different problems, and some of the methodologies used to study them. This course will allow students to gain both skills and a greater understanding of public health research and practice as it applies to international health. prereq: [6320 or 6341, instr consent] master's or doctoral level student in School of Public Health

Our understanding of human genomic variation and its relationship to health is expanding rapidly. This knowledge is now being translated primarily through the field of ?precision medicine? (finding the right drug for the right person at the right time). Public health, in contrast, seeks to abate the social and environmental factors that lead to disease and health disparities. This course will provide an introduction to the field of public health genomics at this interesting point in its history. Approximately one-half of the course is devoted to Genetic Epidemiology, or the science of detecting genetic risk factors for human disease. The other half of the course will cover public health genomics, including ?precision public health?, genetic screening programs, and the possibilities and pitfalls of direct to consumer marketing of genetic tests. How genomics relates to health equity will be a recurring theme of this course. This is a graduate course designed primarily for Epidemiology MPH and PhD students, and fulfills the ?Epi Of? requirement for the MPH in Epidemiology. Graduate students from other programs are very welcome.

Principles and/ methods. Strategies for disease control and prevention, including immunization. Relevance of modes of transmission of specific agents for disease spread and prevention. Public health consequences of infectious diseases at local, national, and international levels.

The course will provide an introduction to cardiovascular disease (CVD) epidemiology. It is intended to provide a detailed perspective on the well-established risk factors for CVD, as well as an introduction to emerging risk factors. Both observational studies and clinical trials will be discussed. The class will include a main focus on prevention of cardiovascular disease, and national recommendations for treatment and prevention. Several classes will incorporate discussions of new directions and current controversies in CVD. Additionally, the class will introduce students to the CVD research in the Division of Epidemiology and Community Health.

Epidemiologic aspects of cancer. Theories of carcinogenesis, patterns of incidence and mortality, site-specific risk factors. Issues of cancer control and prevention.

Nutrition/disease relationships through application of epidemiologic methods. Characterization of various exposures to food/nutrient intakes, biological basis for nutrition/disease relationships. Studies of specific chronic diseases and nutritional intake. Design/interpretation of studies using nutritional measures. prereq: [[6320 or 6330 or 6341], [Epidemiology MPH or Public Health Nutrition MPH or Epidemiology PhD student]] or instr consent

T-tests, confidence intervals, power, type I/II errors. Exploratory data analysis. Simple linear regression, regression in matrix notation, multiple regression, diagnostics. Ordinary least squares, violations, generalized least squares, nonlinear least squares regression. Introduction to General linear Model. SAS and S-Plus used. prereq: [[Stat 5101 or concurrent registration is required (or allowed) in Stat 5101], biostatistics major] or instr consent

This course introduces students to a variety of concepts, tools, and techniques that are relevant to the rigorous design and analysis of complex biomedical studies. Topics include ANOVA, sample-size calculations, multiple testing, missing data, prediction, diagnostic testing, smoothing, variable selection, the bootstrap, and nonparametric tests. R software will be used. Biostatistics students are strongly encouraged to typeset their work using LaTeX or in R markdown. prereq: [7405, [STAT 5102 or concurrent registration is required (or allowed) in STAT 5102], biostatistics major] or instr consent

Hypotheses/endpoints, choice of intervention/control, ethical considerations, blinding/randomization, data collection/monitoring, sample size, analysis, writing. Protocol development, group discussions. prereq: 6414 or 6450 or one semester graduate-level introductory biostatistics or statistics or instr consent

Correlated data arise in many situations, particularly when observations are made over time and space or on individuals who share certain underlying characteristics. This course covers techniques for exploring and describing correlated data, along with statistical methods for estimating population parameters (mostly means) from these data. The focus will be primarily on generalized linear models (both with and without random effects) for normally and non-normally distributed data. Wherever possible, techniques will be illustrated using real-world examples. Computing will be done using R and SAS. prereq: Regression at the level of PubH 6451 or PubH 7405 or Stat 5302. Familiarity with basic matrix notation and operations (multiplication, inverse, transpose). Working knowledge of SAS or R (PubH 6420).

Introduction to Bayesian methods. Comparison with traditional frequentist methods. Emphasizes data analysis via modern computing methods: Gibbs sampler, WinBUGS software package. prereq: [[7401 or STAT 5101 or equiv], [public health MPH or biostatistics or statistics] grad student] or instr consent

Introduction to statistical problems arising in molecular biology. Problems in physical mapping (radiation hybrid mapping, DDP), genetic mapping (pedigree analysis, lod scores, TDT), biopolymer sequence analysis (alignment, motif recognition), and micro array analysis. prereq: [6450, [6451 or equiv]] or instr consent; background in molecular biology recommended

Statistical methodologies in analysis of survival data. Kaplan-Meier estimator, Cox's proportional hazards multiple regression model, time-dependent covariates, analysis of residuals, multiple failure outcomes. Typical biomedical applications, including clinical trials and person-years data. prereq: 7405, [STAT 5101 or STAT 8101]

Diagnostic medicine, including methods for ROC curve. Bioassays. Early-phase clinical trials, methods including dose escalation, toxicity, and monitoring. Quality of life. prereq: [[6450, 6451] or equiv], [grad student in biostatistics or statistics or clinical research], familiarity with SAS

Various statistical techniques for extracting useful information (i.e., learning) from data. Linear discriminant analysis, tree-structured classifiers, feed-forward neural networks, support vector machines, other nonparametric methods, classifier ensembles, unsupervised learning. prereq: [[[6450, 6452] or equiv], programming backgroud in [FORTRAN or C/C++ or JAVA or Splus/R]] or instr consent; 2nd yr MS recommended

Although most of statistical inference focuses on associational relationships among variables, in many biomedical and health sciences contexts the focus is on establishing the causal effect of an intervention or treatment. Drawing causal conclusions can be challenging, particularly in the context of observational data, as treatment assignment may be confounded. The first part of this course focuses on methods to establish the causal effect of a point exposure, i.e., situations in which treatment is given at a single point in time. Methods to estimate causal treatment effects will include outcome regression, propensity score methods (i.e., inverse weighting, matching), and doubly robust approaches. The second half of the course focuses on estimating the effect of a series of treatment decisions during the course of a chronic disease such as cancer, substance abuse, mental health disorders, etc. Methods to estimate these time-varying treatments include marginal structural models estimated by inverse probability weighting, structural nested models estimated by G-estimation, and the (parametric) G-computation algorithm. We will then turn our attention to estimating the optimal treatment sequence for a given subject, i.e., how to determine “the right treatment, for the right patient, at the right time,” using dynamic marginal structural models and methods derived from reinforcement learning (e.g., Q-learning, A-learning) and classification problems (outcome weighted learning, C-learning). PubH 8485 is appropriate for Ph.D students in Biostatistics and Statistics. The homework and projects will focus more on the theoretical aspects of the methods to prepare students for methodological research in this area. PubH 7485 is appropriate for Masters students in Biostatistics and PhD students in other fields who wish to learn causal methods to apply them to topics in the health sciences. This course uses the statistical software of R, a freely available statistical software package, to implement many of the methods we discuss. However, most of the methods discussed in this course can be implemented in any statistical software (e.g., SAS, Stata, SPSS, etc.) and students will be free to use any software for homework assignments. prereq: Background in regression (e.g. linear, logistic, models) at the level of PubH 7405-7406, PubH 6450-6451, PubH 7402, or equiv. Background in statistical theory (Stat 5101-5102 or PubH 7401) is helpful.