Course Descriptions

This course provides an introduction to the fundamentals of research in clinical epidemiology. It covers definitions of epidemiology; measures of disease frequency; measures of effect and association; epidemiologic study designs, including randomized clinical trials, cohort and case-control studies, cross-sectional surveys, and meta-analysis; and an overview of the conduct and analysis of epidemiologic studies. The course is composed of a series of 2-hour lectures and in-class lab sessions, designed to reinforce concepts introduced in the lectures. 

As infectious diseases are transmitted from one host to another, the dynamics of transmission in the population of hosts follow certain basic rules. If one knows and understands these rules, one can plan rational strategies to prevent or control infections. One of the principal tools of those interested in public health interventions to control or ameliorate infectious diseases is the mathematical model. A model is just a means of representing and manipulating something that would not otherwise be accessible. This elective course provides students with the opportunity to construct models of the transmission of infectious diseases and to use these models to plan or compare disease control strategies. The course is predicated upon the notion that the act of building a mathematical model of disease transmission is often the very best way of understanding what is going on. This understanding will be further refined by the examination of more complicated and sophisticated model structures as they appear in the recent published literature.  

This course will provide an introduction to GIS in public health research and practice. Through a series of lectures and labs students will explore theories linking health and the environment, spatial analysis/epidemiology, and applications of GIS-related data collection and analysis.

The first half of this course will cover graphical methods, probability, discrete and continuous distributions, estimation, confidence intervals, and one-sample hypothesis testing. Emphasis is placed on understanding the proper application and interpretation of the methods. The second half of this course will cover two-sample hypothesis testing, nonparametric techniques, sample size determination, correlation, regression, analysis of variance, and analysis of covariance. Emphasis is placed on understanding the proper application and underlying assumptions of the methods presented. Laboratory sessions focus on the use of the STATA statistical package and applications to clinical data.

The first half of this course covers concepts in biostatistics as applied to epidemiology, primarily categorical data analysis, analysis of case-control, cross-sectional, cohort studies, and clinical trials. Topics include simple analysis of epidemiologic measures of effect; stratified analysis; confounding; interaction, the use of matching, and sample size determination. Emphasis is placed on understanding the proper application and underlying assumptions of the methods presented. Laboratory sessions focus on the use of the STATA and other statistical packages and applications to clinical data. The second half of this course covers concepts in biostatistics as applied to epidemiology, primarily multivariable models in epidemiology for analyzing case-control, cross-sectional, cohort studies, and clinical trials. Topics include logistic, conditional logistic, and Poisson regression methods; and simple survival analyses including Cox regression. Emphasis is placed on understanding the proper application and underlying assumptions of the methods presented. Laboratory sessions focus on the use of STATA and other statistical packages and applications to clinical data.

This course combines informal lecture and discussion with practical exercises to build specific skills for conducting qualitative research on healthcare, broadly defined. Readings include books and papers about research methodology and articles that provide exemplars and pitfalls of qualitative research. Specific topics covered include: the role of theory in qualitative research, method-research question fit, collecting different types of qualitative data (observation, interview, focus group, text, video), ethical issues in qualitative research, establishing rigor in qualitative research, introduction to qualitative data analysis using software, mixing methods, approaches for obtaining grant funding for qualitative research and writing up qualitative research studies for publication.

The objective of this two-course series is to enhance MSCE students' comfort and acumen in all aspects of clinical epidemiological data management and presentation, particularly graphical representation of results. The course progresses from best practices in data collection and database use to advanced data management, summarization of results, and data visualization, all of which are grounded in the prioritization of producing efficient and reproducible research processes. The course will cover and develop skills in: basic data collection, harmonization, and integration with Stata software; best practices for data variable derivation and creation; assessing and dealing with missing data; merging and appending datasets; management of dates and times; assessing free text data; dealing with specific data types such as ICD-9 and 10 codes, cost data, management of longitudinal and time-to-event data; production of descriptive and regression tables (for all regression types); descriptive and regression model visualization; and the use of Stata Markdown files such that research reports can be created directly from Stata. By the end of the two-course series, students will become fluent in the Stata statistical language and be uniquely positioned to advance their independent clinical epidemiological careers through best research and data presentation practices.

The objective of this two-course series is to enhance MSCE students' comfort and acumen in all aspects of clinical epidemiological data management and presentation, particularly graphical representation of results. The course progresses from best practices in data collection and database use to advanced data management, summarization of results, and data visualization, all of which are grounded in the prioritization of producing efficient and reproducible research processes. The course will cover and develop skills in: basic data collection, harmonization, and integration with Stata software; best practices for data variable derivation and creation; assessing and dealing with missing data; merging and appending datasets; management of dates and times; assessing free text data; dealing with specific data types such as ICD-9 and 10 codes, cost data, management of longitudinal and time-to-event data; production of descriptive and regression tables (for all regression types); descriptive and regression model visualization; and the use of Stata Markdown files such that research reports can be created directly from Stata. By the end of the two-course series, students will become fluent in the Stata statistical language and be uniquely positioned to advance their independent clinical epidemiological careers through best research and data presentation practices.

Please check with Department as this course is not offered every year.  This course offers students an introduction to the field of injury and violence prevention. As a major cause of death and disability throughout the world, injury is a leading public health problem. Prominent types of injuries to be discussed include those relating to motor vehicles, falls, and firearms. Behavioral, biological, economic, and social issues concerning the implementation of injury reduction policies are emphasized through case studies as well as injuries in the workplace, in the home, and during recreation.

This course addresses the measurement of epidemiological variables, which broadly encompasses the tasks involved in obtaining data, without which analyses cannot proceed. Course topics to be discussed include: defining the concepts of exposure, disease, and health; approaches to measuring exposures, which may be personal (i.e., psychological, behavioral, biological, or genetic) or environmental (i.e., physical, chemical, social, or organizational); approaches to measuring disease and health status; assessing the validity and reliability of measurement instruments; problems of misclassification of exposure status and disease status; missing data; instrument (e.g., questionnaire) development; and qualitative methods. 

This course will discuss appropriate selection of healthcare databases for research questions of interest; assessment of drug exposures; validation of health outcomes of interest; and addressing biases, confounding, and missing data in databases. We will also review key aspects of research protocol development for database studies and discuss research grant applications related to these studies.

This course focuses on the application of decision analysis and economic analysis to clinical and policy research. It provides an introduction to the general tools for decision analysis, including decision trees and Markov models, assessment of costs and patient preferences, and assessment of cost-effectiveness. Special emphasis is placed on second-order Monte Carlo analysis and its use in the construction of measures of sampling uncertainty for cost-effectiveness analysis. Seminars will include didactic material, practical exercises that include problem solving, critically analyzing published articles and learning to use computer software that facilitates decision and economic analyses.

This seminar focuses on major issues in research protocol development, including methodologic issues regarding different research designs, development of research questions, and plans for analysis. Each student will present his or her research protocol for open discussion during one of the seminar sessions.

This seminar focuses on techniques for critical appraisal of the medical literature. Each student will be responsible for at least one critical appraisal session covering different epidemiologic topics (including the evaluation of diagnostic tests, clinical course and prognosis of disease, disease etiology or causation, therapy, quality of clinical care, economic evaluation, and meta-analysis). Each week, a student will critically appraise a journal article and lead the discussion concerning that article.

Recent advances have made it feasible to incorporate data on potential genetic risk factors into traditional epidemiologic studies. Hence, there is an increasing need for epidemiologists to understand the genetic basis of disease, read, and interpret genetic studies, and incorporate the collection and analysis of genetic information into studies of disease etiology. The objectives of this course are to provide epidemiologists with an understanding of: 1) basic genetics, 2) the tools used by geneticists and genetic epidemiologists, and 3) the integration of genetic data into traditional epidemiologic study designs. After completing this course, students will be able to read and interpret genetic epidemiologic studies. In addition, they will be able to design epidemiologic studies that incorporate genetic data collection and analysis.

This course is divided into two main parts. The first part addresses issues related to the measurement of quality in health care. Included is a review of the classical structure-process-outcome quality paradigm. The paradigm’s strengths and limitations are addressed. This part especially focuses on outcome measures of quality, and examines the validity of alternative measures. The second part deals with observational, or quasi-experimental, research studies. It addresses the advantages and limitations of alternative designs, and covers the role of clinical risk adjustment in observational studies of medical interventions. It focuses on the problem of selection bias, and reviews recent methods for dealing with this bias, such as instrumental variables.

This course will provide an overview of research in health disparities. It will cover the historical aspects, concepts, policy, economic, genomic and social perspectives of health disparities. It will provide students with methodological tools for health disparities research and introduce students to ongoing health disparities research by current Penn and affiliated faculty members. The course is composed of a series of weekly small group lectures and discussion, including critical appraisal of published papers, guest faculty presentations, and student presentations. Students will be expected to attend weekly meetings and participate in class discussions, prepare and lead discussions of assigned papers, review assigned readings, and draft and present a scientific protocol of their choosing related to health disparities.

Data science refers broadly to using statistics and informatics techniques to gain insights from large datasets. Biomedical informatics refers to a range of disciplines that use computational approaches to analyze biomedical data to answer pre-specified questions as well as to discover novel hypotheses. In this course, we will use R and other freely available software to learn fundamental data science applied to a range of biomedical informatics topics, including those making use of health and genomic data. After completing this course, students will be able to retrieve and clean data, perform exploratory analyses, build models to answer scientific questions, and present visually appealing results to accompany data analyses; be familiar with various biomedical data types and resources related to them; and know how to create reproducible and easily shareable results with R and github.

This is a tutorial given by each student’s MSCE mentor. The mentor and student meet regularly, usually weekly. Topics include discussion and review of epidemiologic concepts and principles, guided readings in the epidemiology of a specific health area, and the development of the research protocol. Credit for this course is awarded upon completion of a research project protocol, the one to be used to fulfill the MSCE thesis requirement, which must be approved by the student’s mentor. Evaluation is based on the grade received for the protocol.

An introduction to the principles of and methods for longitudinal and clustered data analysis with special emphasis on clinical, epidemiologic, and public health applications. Designed for advanced MS and PhD-level students in epidemiology and related fields. Marginal and conditional methods for continuous and binary outcomes. Mixed effects and hierarchical models. Simulations for power calculations. Software will include Stata and R. Prerequisite: Completion of EPID 5260 and 5270 or equivalent preparation in biostatistics, including generalized linear models. Completion of semester curse in principles of epidemiology or equivalent. Good working knowledge of Stata and SAS and familiarity with principles of first-year calculus and matrix algebra. Permission of course director.

This course will provide in-depth treatment of several topics in categorical data analysis. After a brief review of methods for contingency tables, we will introduce the idea of generalized linear models, and focus on two special cases – multiple logistic regression and log-linear models. Each topic will be presented in detail by stating the model and covering parameter estimation and interpretation, inference, model building, regression diagnostics, and assessment of model fit. Finally, we will cover extensions to both models, including models for multinomial data, analysis of matched-pair data, and random effects models. Topics will be illustrated in class with examples, and we will discuss the use of Stata to conduct the analyses.

This course will focus on the specialized issues related to the analysis of survival or time-to-event data. The course begins by closely examining the features unique to survival data that distinguish these data from other more familiar types. Topics include non-parametric survival analysis methods, common survival functions, parametric survival models, the proportional hazards model, and common model-checking methods. All methods will be illustrated by in-class examples and homework sets. Prerequisite: Students should be comfortable with basic calculus concepts (e.g., derivatives, integrals, etc).

The goal of this course is to provide a broad overview of methods used in patient centered outcomes and effectiveness research. Expert faculty will lecture on topics such as standards for research questions, systematic reviews, patient/stakeholder engagement, causal inference, heterogeneity of treatment effect, handling missing data, data registries, pragmatic trials, diagnostic tests, health care disparities, evaluating the impact of communication interventions, and testing innovations in health care systems. Grading will largely be based on participation in class discussions.

This course is an introduction to statistical methods that can be used to evaluate biomarker prognostic studies and multivariate prediction models. Topics will include biostatistical evaluation of biomarkers, predictive models based on various regression modeling strategies and classification trees, assessing the predictive ability of a model; internal and external validation of models; and updating prognostic models with new variables or for use in different populations. Students will learn about the statistical methods that are required by current reporting guidelines for biomarker prognostic studies or the reporting guidelines for multivariable prediction models. 

This course is to serve as a general introduction to clinical trials and will emphasize trial design issues. This is not a course on the biostatistics of clinical trials. It is expected that at the conclusion of the course, a student will be able to plan a clinical trial. Each class will consist of a two-hour lecture followed by a one-hour discussion. The weekly session will focus on either a group discussion of the assigned reading or a practical application based on the material presented during the two-hour lecture. Students will be evaluated on their participation in class (20%); a clinical trial document (50%), which should include the rationale for the study, study design, objectives and endpoints, sample size and analysis sections, and consent form; and a class presentation of their trial or another topic (30%).

This course is intended to teach students the skills necessary to select and/or design appropriate outcomes for a clinical trial. Students will focus on recent changes in our understanding of clinical trial outcome measurements, analyses, and interpretation for both subjective and objective phenomena, such as adherence, use of multiple outcomes, and clinical importance. While design issues for clinical trials are the main focus, other types of clinical studies will be considered as appropriate. Students will be expected to learn about the problems inherent in the design of outcome measures of health and how to apply different epidemiologic and biostatistical concepts toward a solution. It is expected that at the conclusion of the course, students will be able to plan a clinical trial with a valid, responsive and interpretable outcome. The class will meet once weekly for a 60-minute lecture on a topic, followed by a 60- to 90-minute discussion of how that topic applies to the specific issues of interest to the students or the instructor.

This is an advanced course addressing epidemiologic issues as they apply to important clinical topics in acute care, including emergency, hospital, and critical care medicine. Lectures and discussions will have two primary goals: 1) to explore epidemiologic methods specific to acute care settings (i.e., choice of outcomes, risk adjustment); and 2) to explore the epidemiology of particular diseases (e.g., sepsis, acute lung injury, hospital acquired infections) and research questions of current importance in these areas. This course will acquaint students with the classic literature in the field of adult and pediatric urgent care, emergency medicine, and critical care epidemiology, teach advanced epidemiologic principles using a problem-based approach, and demonstrate the strengths and weaknesses of epidemiologic research methodologies as they have been applied to acute care.

This course is intended to follow and be complementary to EPID 6300: Clinical Trials. It will build on the basic principles of design, conduct, and analysis introduced in that course and will go into more detail on particular approaches. Topics covered will include noninferiority trials, phase 1 designs, multi-stage and other adaptive designs, graphical data presentations and current ethical controversies in clinical trials.

This course is designed to introduce students to advanced epidemiologic methods through a series of readings and discussions. The course aims to deepen the students’ understanding of important concepts and controversies in contemporary epidemiology and to enhance their ability to think critically about empirical epidemiologic research. The course is intended for students who are already familiar with the fundamentals of epidemiology and biostatistics, and who wish to gain an understanding of the complex issues underlying epidemiologic study design and interpretation. Each week, one student will be responsible for leading a portion of the discussion of the assigned readings, in conjunction with a faculty member. Topics include: causal inference; study designs; use of large databases for research; predicting outcomes; and complex sampling methods.

This is an advanced course that addresses epidemiologic research issues as they apply to important clinical topics in cardiovascular and pulmonary medicine. Lectures and workshops are designed to acquaint students with the classic literature in the fields of cardiovascular and pulmonary epidemiology, to use a body of literature to demonstrate the strengths and weaknesses of epidemiologic research designs as they have been applied to cardiovascular and pulmonary medicine, to expose students to the range of topics studied, to teach advanced epidemiologic principles using a problem-based approach, and to stimulate students to develop independent research questions.

Research in cancer etiology, prevention, treatment, and control includes a wide range of subject matter science, from the initial molecular changes that precede the development of cancer to issues of primary care guidelines for cancer survivors. The course reviews the possible study designs applied to cancer etiology, prevention, treatment, and control. These include randomized controlled trials and multiple types of observational studies (cohort, case-control, cross-sectional). Other topics will include causal inference, bias, and effect modification.

This is an advanced course that addresses epidemiologic research issues as they apply to important clinical topics in obstetrics and gynecology and related clinical disciplines. Lectures and workshops are designed to acquaint students with important issues in the field of reproductive epidemiology, to use a body of literature to demonstrate the strengths and weaknesses of epidemiologic research designs as they have been applied to obstetrics and gynecology and related clinical disciplines, to expose students to the range of topics studied, to teach advanced epidemiologic principles using a problem-based approach, and to stimulate students interested in reproductive epidemiology to develop independent research questions.

The objective of this course is to prepare students to function as effective, independent researchers in the fields of renal and urologic epidemiology by providing the students an understanding of how epidemiologic research can and has advanced the knowledge of diseases in treatments of renal and urologic medicine. The structure of the course consists of a lecture series, accompanying workshops, and student presentations. The goals of the course are to acquaint students with some of the classic literature in the fields of renal and urologic epidemiology; to use a body of literature to demonstrate the strengths and weaknesses of epidemiologic research designs as they have been applied to renal and urologic medicine; to teach advanced epidemiologic principles using a problem-based approach; to expose students to the rationale of topics studied by faculty in the CCEB and the adult and pediatric nephrology and urology divisions at Penn and CHOP; and to stimulate students interested in renal and urologic epidemiology so that they may develop independent research questions.

This will be an advanced course addressing epidemiologic issues as they apply to important clinical topics in infectious diseases. Lectures and discussions will serve two primary goals: 1) to explore epidemiologic methods specific to infectious diseases (e.g., adherence to therapy) or that have important applications to infectious diseases (e.g., molecular epidemiology); and 2) to explore the epidemiology of particular infectious diseases or syndromes (e.g., HIV). This course will acquaint students with the classic literature in the field of infectious diseases epidemiology, teach advanced epidemiologic principles using a problem-based approach, and demonstrate the strengths and weaknesses of research methodologies as they have been applied to infectious diseases.

This course provides an in-depth presentation of advanced methodologic issues in conducting clinical epidemiologic research in the field of gastroenterology.

This course will introduce students to methods and study design principles that are specific or unique to clinical research and trials in neurology, child neurology, neuro-ophthalmology, neurosurgery, and related fields.

The purpose of this course is to explore and integrate concepts and considerations that are key to the conduct of pharmacoepidemiologic research. The format will be a mixture of seminar, instructor-led discussion, student-led discussion, and student presentations. Papers from the applied and methods literature will be used to illustrate concepts and as springboards for discussion. Topics covered include use of automated databases/pharmacogenomics, and approaches to addressing confounding.

This course is designed to teach epidemiology students the statistical principles of analysis specific to pharmacoepidemiology study designs including the use of propensity scores, inverse probability weighting, instrumental variables and time varying covariates. Each of the twelve sessions includes both a lecture component and a laboratory component. Students will learn the statistical principles and then apply them to example study datasets. Students must participate in all sessions and must have previously completed biostatistics for epidemiologic methods I and II (EPID 5260 A/B and 5270 A/B). Laboratory sessions will be conducted on students’ laptops using STATA software.

This is an advanced course addressing the methods and tools used to analyze microbiome data, as well as their implications for clinical study design. The course will include: (1) lectures focused on how the microbiome is measured, approaches to the analysis of highly multivariate microbiome data, and the bioinformatic tools used to execute these analyses; (2) hands-on R and command-line coding to build familiarity with commonly used tools and analytic methods; and (3) short, practical assignments to reinforce the lectures and classwork. The course will acquaint students with classic literature in the field of microbiome research and prepare students to integrate microbiome data collection and analysis with epidemiologic research methodologies.

The recent explosion in the availability of molecular level data coupled with technological advancements allowing for large-scale sequencing creates an exciting opportunity to tailor treatment decisions to the specific genetic characteristics of a patient. Epidemiologic studies will provide the tools to draw from this vast array of molecular data as well as well-established environmental risk factors to predict disease outcomes. However, understanding analytic methods for characterizing the complex interactions among genetic polymorphisms, biomarkers, environmental factors, and disease outcomes is imperative to draw meaningful and relevant conclusions from these studies. Through this course, students will understand and present advanced statistical methods and how they can be applied to the study of complex genetic traits.

The course is intended to meet the needs of PhD students over the entire program from the coursework phase through the dissertation defense, and is intended to optimize cross-fertilization between the students at all phases of their program. Restricted to Epidemiology Doctoral Students. 

Students will devote at least 100 hours working with faculty on projects related to the Center for Clinical Epidemiology and Biostatistics' Consultation Service or on other projects as approved by the course director.  The goal of this course is to provide real-world consulting experience for doctoral students.  Accordingly, the requirements for the course cannot be met by serving as a teaching assistant or working in the role of an analyst or other project staff.

Environmental Epidemiology is an advanced epidemiology course that addresses epidemiological research methods used to study environmental exposures from air pollution to heavy metals, and from industrial pollutants to consumer product chemicals. The course will provide an overview of major study designs in environmental epidemiology, including cohort studies, panel studies, natural experiments, randomized controlled trials, time-series, and case-crossover studies. The course will discuss disease outcomes related to environmental exposures, including cancer and diseases of cardiovascular, respiratory, urinary, reproductive, and nervous systems. Case studies in environmental epidemiology will be discussed to provide details of research methods and findings.

This course is designed to provide background and guidance on writing and submitting NIH grants. Students will submit a mini-protocol proposal at the beginning of the term. Each protocol will be reviewed by a group of 3 students from the class and scores will be given. The final project will be a full NIH protocol proposal ready for submission.

This modular course meets the needs of students who require specialized instruction and hands-on training in specific topics that are not available in a traditional course setting. Multiple modules are available, providing advanced training in specific methods in genetic and molecular epidemiology, including the possibility of laboratory rotations to obtain hands-on laboratory experience. Each student may choose up to four modules for study during the semester-long course. Each module includes readings, meetings with faculty, problem sets, laboratory analysis, or analysis of data, as appropriate.

These are a series of tutorial sessions conducted by the student’s mentor intended to support the student’s efforts in developing a research protocol, designing a research project, and completing the study.

This is a preceptorship that can be arranged with any of the CCEB faculty. The subject area and specific requirements are to be arranged as well.