Courses

PBHL 630: INTERMEDIATE EPIDEMIOLOGY (3 credits)

This course expands on basic methods used in epidemiologic thinking and research – with a focus on observational studies of disease risk factors. Topics covered include: basic principals of causal inference; observational study designs; bias; confounding; effect modification; stratified analysis; and the epidemiologic approach to multivariable modeling. An emphasis is also placed on critically reading the epidemiologic literature.

PBHL 632: APPLIED SURVEY RESEARCH IN EPIDEMIOLOGY (3 credits)

This course addresses theoretical/practical aspects pertinent to the conduct of survey research in human populations. Topics include sampling, recruitment, and enrollment strategies; selection, definition, and measurement of study variables; instrument development/design; data collection techniques/requirements; data file development/management activities; and issues related to the influence of survey study design/execution on epidemiological effect measures.

PBHL 633: CANCER EPIDEMIOLOGY (3 credits)

This course will provide students with training in the methods and topics specific to the epidemiology of cancer. Students will learn about cancer surveillance, etiologic studies, therapy trials, and prevention/screening studies of cancer.

PBHL 634: EPIDEMIOLOGY FOR PUBLIC HEALTH PRACTICE (3 credits)

This course is designed to enable the student to understand epidemiology as a health discipline and how epidemiology provides information for infectious/non-infectious disease prevention and control. Topics cover public health surveillance, outcomes research, health services research, principles of cancer registration, and a variety of practice-related exercises.

PBHL 635: SOCIAL AND PSYCHIATRIC EPIDEMIOLOLGY (3 credits)

The course addresses the content and methods of social epidemiology and the clinical, methodological, and epidemiologic aspects of psychiatric illness. Students are required to explore theoretical and empirical aspects of disease etiology and disease course that extends beyond a biomedical model.

PBHL 636: INFECTIOUS DISEASE EPIDEMIOLOGY (3 credits)

This course introduces epidemiologic methods specific to infectious disease epidemiology within the context of the study of several major classes of infectious diseases with global impact on public health. Students will learn about techniques in outbreak investigations as well as surveillance and disease reporting. They will learn how biological characteristics of infectious diseases such as transmission and immunity alter the more familiar approaches to descriptive and analytic epidemiology developed in the chronic disease setting.

PBHL 638: PERINATAL EPIDEMIOLOGY (3 credits)

Introduces topical issues and methodological approaches to studying maternal and child health outcomes during the perinatal period. The focus is on study designs and data sources most relevant to perinatal epidemiology and examples of epidemiologic research on common perinatal health issues. Current research areas in perinatal epidemiology and future directions for research are also presented.

PBHL 639: CARDIOVASCULAR DISEASE EPIDEMIOLOGY (3 credits)

This course provides a forum for in-depth discussions of one of the main public health issues. Topics include the pathophysiology of atherosclerosis and cardiovascular disease (CVD), trends in coronary heart disease, stroke, hypertension and heart failure mortality/morbidity, well-established and emerging CVD risk factors, and major strategies for CVD prevention/control.

PBHL XXX: CHALLENGES TO CAUSAL INFERENCE IN EPIDEMIOLOGIC RESEARCH (3 credits)

This course is designed to provide a theoretical foundation and the practical tools necessary for addressing challenges to causal inference in epidemiological research. Upon successful completion of this course, students will be able to 1) Understand causal inference problems through the framework of potential outcomes and assignment mechanisms; 2) Identify challenges to causal inference, including missing data, measurement error, confounding, and selection bias; 3) Address identified challenges using analytical methods, such as multiple imputation, regression calibration, propensity score adjustment, and marginal structural models; 4) Assess sensitivity of inferences to complex methodological problems, such as those listed above.

PBHL 826: CAUSAL INFERENCE IN EPIDEMIOLOGY (3 credits)

Provides an in-depth theoretical foundation on epistemology and models of disease causation in epidemiology. Students will be expected to answer the question how can we know that A causes B from diverse perspectives ranging from theoretical models, statistical conventions around identifying causation, and mitigating bias.

PBHL 830: ADVANCED EPIDEMIOLOGY (4 credits)

This course covers more advanced methodologic issues in analytic epidemiology including: in-depth discussions of cohort, case-control, and case-cohort studies, missing data and methods of single/multiple imputation, theoretical basis of and analytic methods for using intermediate endpoints/surrogate markers, repeated measures analysis, the use of DAGS, and propensity scores to mitigate confounding.

PBHL 520 BIOSTATISTICS (4 credits)

Introduces and applies the biostatistics tools and analytical base for population-based and community health assessment and evaluation. The focus is on providing a broad and basic understanding of biostatistics, with more advanced methods included as appropriate.

PBHL 620 INTERMEDIATE BIOSTATISTICS I (3 credits)

Intermediate Biostatistics is a required course for the Master of Public Health (MPH) Program of Study, Concentration in Epidemiology and in Biostatistics. It covers topics in epidemiological statistics, nonparametric statistics, consulting skills, choices of techniques, and data cleaning.

PBHL 621: INTERMEDIATE BIOSTATISTICS II (3 credits)

This course reinforces the basic biostatistics and data management skills acquired in the Intermediate Biostatistics I course. The main focus will be on model assumption checking and fit assessment; however specialized topics like modeling variable with more than two levels and repeated measures will be covered.

PBHL 622: INTRODUCTION TO BIOSTATISTICS THEORY (3 credits)

This is an introductory course in probability and the theory of biostatistics that include the introduction of the probability distributions, as well as focusing on underlying theoretical foundations.

PBHL 623: BIOSTATISTICS COMPUTING (3 credits)

Trains students in data management and graphical presentation skills so that they can independently manage small to intermediate sized research data bases. Statistical packages SAS and R will be covered.

PBHL 625: LONGITUDINAL DATA ANALYSIS (3 credits)

Covers statistical methods and software commonly used to analyze longitudinal or repeated measurements data that are often encountered in public health and biomedical studies.

PBHL 628: SURVIVAL ANALYSIS (3 credits)

This course will provide the students with different approaches of analysis of survival data. These techniques are particularly useful in cohort designs studies where the main outcome of interest is the onset of an event and the information time to event is available.

PBHL 629: DESIGN AND ANALYSIS OF CLINICAL TRIALS (3 credits)

The purpose of this course is to cover the design and conduct of clinical trials. The course will also cover how to evaluate the scientific rigor of studies of clinical trials published in the scientific literature. Topics which will include power and sample size, study design, randomization methods, recruitment, missing data, ethical issues and statistical analysis methods.

PBHL 631: APPLIED MULTIVARIATE STATISTICS (3 credits)

This course introduces students to statistical methods for describing and analyzing multivariate data. Topics include basic matrix algebra; multivariate normal distribution; linear models with multivariate response; multivariate analysis of variance; profile analysis; dimension reduction techniques, including principal component analysis, factor analysis, canonical correlation, multidimensional scaling; discriminate/cluster analysis; and classification/regression trees.

PBHL 683: ADVANCED CLINICAL TRIALS & EXPERIMENTAL DESIGN (3 credits)

This course prepares students to design & conduct clinical trials and other health related experiments. It will cover the development of a study protocol for a clinical trial, selection of the study population, sample size, and treatment assignment methods. Advanced experimental designs will also be covered.

PBHL 684: BIOSTATISTICS THEORY II (4 credits)

This course is a continuation of Biostatistics Theory I focusing on concepts and methods of statistical inference. Topics include point/interval estimation, methods of moments, maximum likelihood estimation, Bayes estimates, hypothesis testing, Neyman-Pearson lemma, likelihood ratio tests and large sample approximation, and Bayesian analysis.

PBHL 686: ADVANCED STATISTICAL COMPUTING (3 credits)

This course expands on computational methods used in biostatistics. It covers numerical techniques, programming, and simulations and will connect these to fundamental concepts in probability and statistics. The course will use the statistical software, R, to apply these concepts and enable the practical application of biostatistical models to real-world problems.

PBHL 687: READINGS IN BIOSTATISTICS (1 credit)

Guided readings course designed to introduce MS Biostatistics students (and other interested students) to classic papers in Biostatistics. Provides students with exposure to classic biostatistics papers and practice critically reading statistics literature. Also exposes students to some issues relevant to the practice of biostatistics that are not covered in coursework.

PBHL 688: BIOSTATISTICS THEORY LAB (1 credit)

This course is a complement to Biostatistics Theory I in the sense that statistical concepts and methods will be developed in a mathematical framework and also additional topics will be discussed as time permits. Topics tentatively selected include: distributions, conditional distributions and expectation, probability inequalities/identities, limit theorems, and Bayesian methods.