PCE students are required to enroll in integrated summer-long core courses in Clinical Epidemiology and Biostatistics. These applied courses are directed at clinical investigators and provide the fundamental skills needed for clinical research. These courses are inter-related, present the students with exercises in “active learning,” and provide experience in many aspects of clinical research. Because they are core to the program, they meet daily throughout the six weeks.
The Clinical Epidemiology course provides training in methods for designing and interpreting results from studies as well as a mentored group experience in which students design a research project of their choice. This involves more than 50 experienced clinical investigators who offer small group workshops and individual office hours. In addition, students receive valuable feedback through oral and written presentations of their study design. The Clinical Biostatistics course provides applied training in fundamental analytical skills with supportive training with commonly used computer packages.
All participants in the PCE also take two elective courses. There are two summer sessions during the program; each student takes one elective in each of the summer sessions. Elective courses in previous years have included:
Linear and Longitudinal Regression (BST 215)
Effectiveness Research with Longitudinal Healthcare Databases (EPI 253)
Improvement in Quality in Health Care (HPM 253)
Introduction to Methods and Applications in Health Services Research (HPM 276)
Implementation Research in Health and Healthcare (HPM 284)
Research with Large Databases (HPM 299)
Medical Informatics (HPM 512)
Decision Analysis in Clinical Research (RDS 286)
Methods for Decision Making in Medicine (RDS 288)
Collaborative Data Science in Healthcare (BST 209)
Please note, course offerings are subject to change.
Summer Session in Public Health Courses
In addition to the PCE course offerings listed above, students may also select from the Summer Session for Public Health Studies course offerings to fulfill the Program’s afternoon elective requirements.
Advanced PCE Courses
Upon completion of the PCE, qualified students in degree programs are eligible to take second-level courses during subsequent summers. These half-summer courses are offered at the same time as the first-year core courses.
Students with previous Master’s-level training may qualify for the PCE certificate by replacing PCE core courses with the PCE advanced courses and electives.
Please note, course offerings are subject to change.
Core Course Descriptions
Introduction to Biostatistics (BST 206/207) provides a detailed introduction to the theory and application of statistical techniques that commonly are used in clinical research. Topics include probability distributions, significance testing, confidence intervals, sample size calculation and power, measures of association, chi-square tests, stratified and matched analyses, t-tests, non-parametric analyses, analysis of variance and the basics of linear regression. By the end of the course, students should be able to conduct all of the basic statistical tests, recognize the assumptions behind their analyses, and interpret the results.
Lectures are supplemented by homework and computing labs to acquaint the participants with different methods for conducting analyses. The SAS, Stata, and R statistical programs will be taught during classes and used to carry out analyses.
Introduction to Clinical Epidemiology (EPI 208) covers core epidemiologic concepts and study designs from the perspective of clinical research. Topics include the design and analysis of cohort and case-control studies, randomized controlled trials, time series and quasi-experimental designs and quality improvement studies; minimization of bias; identification and control of confounding; and assessment of effect modification. Other related topics that are covered include diagnostic test evaluation, screening for disease, measuring quality of life, assessing the reliability and validity of questionnaires, propensity scores, and clinical prediction rules. One session is devoted to the writing of proposals and scientific papers.
Students use this methodologic training to prepare a clinical research study proposal. Students receive feedback from senior investigators during office hours and small-group workshops, make a formal presentation of their research plan during class, and submit a final written proposal in the form of a grant application. Ideally, these proposals provide the foundation for future research projects.
We strongly recommend that students have a few possible ideas for their project in mind before the beginning of the course.
Elective Course Descriptions
Linear and Longitudinal Regression (BST 215) is intended for students who are already very comfortable with fundamental techniques in statistics. The course will cover methods for building and interpreting linear regression models, including statistical assumptions and diagnostics, estimation and testing, and model building techniques. These models will be extended to handle data arising from longitudinal studies employing repeated measurement of subjects over time. Lectures will be accompanied by computing exercises using the Stata statistical package.
Effectiveness Research with Longitudinal Healthcare Databases (EPI 253) Large longitudinal healthcare databases (e.g., claims, electronic health records) have become important tools for studying the utilization patterns and clinical effectiveness of medical products and interventions in a wide variety of care settings and for evaluating the impact of clinical programs or policy changes. This course will prepare students to identify and use longitudinal databases in their own research.
Strengths and limitations of large longitudinal healthcare databases that are commonly used for research will be considered. Special attention will be devoted to nationally representative databases that are critical for comparative effectiveness research and local electronic medical record data sources that are readily available to new investigators.
Practical issues in obtaining, linking, and analyzing large databases will be emphasized throughout the course, and key analytic issues will be addressed, including design considerations and multivariate risk-adjustment. Students will work in teams with faculty advisors to conduct research projects using a nationally representative claims database of 23 million lives. They will work with an easy-to-use software platform that helps them implement complex studies without requiring programming skills.
The course focuses on analytic principles and their application to database research. It requires an understanding of epidemiologic study designs (cohort, case-control) and typical analysis strategies (logistic regression, Cox regression, propensity score analysis).
Improvement in Quality in Health Care (HPM 253) is designed for practicing clinicians and those with an interest in health care management. This interactive and challenging course will provide students with a fresh perspective on improvement in health care systems and provide them with the necessary tools to effect the kind of real change in their own organizations and practices that can improve outcomes for patients. Topics of the sessions will include: systems thinking; the leadership of improvement; statistical thinking and the management of variation; process knowledge and design; change methods, improvement, and design and creativity; collaborative work; matching service design to needs; personal and professional learning and change; the diffusion of innovations; spreading new models of care across organizational silos and boundaries; and integrating cost and quality, and managing resistance to improvement.
Introduction to Methods and Applications in Health Services Research (HPM 276) introduces students to the interdisciplinary field of health services research. The course uses theory, methodology, and applications in a highly interactive teaching approach and is designed for students who lack significant prior experience in research or advanced knowledge of research methods. Individual sessions cover a variety of topics including research design, large databases, cost-effectiveness analysis, surveys and focus groups, assessment of health status, quality measurement, measurement of race, ethnicity, and socioeconomic status, appropriateness of care, risk adjustment, and statistical techniques pertinent to health services research. There will be one or more sessions reviewing managerial applications such as case management, use of hospital information systems, and targeting for high-risk patients.
The course will also include class sessions and exercises devoted to critique of journal articles. These will supplement didactic presentations and will target development of skills in performing research and writing papers. In the final part of the course, students will work in small groups to critique a “grant proposal” designed to study an important problem in health services or health policy research. Each group of students will write up their critique in a format typical for a federal study section. This effort is designed to educate students on important aspects of grant writing.
Implementation Research in Health and Healthcare (HPM 284) introduces students to the study of interventions to facilitate the translation evidence-based interventions into practice. There is a growing awareness that studies on comparative and cost effectiveness, which identify practices that will maximize quality and value, require companion work on implementation research to assure that current evidence is ultimately implemented into real-world clinical settings and health policy. This course is intended to provide an introduction to the theory and methods that address the facilitators and barriers to the translation of evidence into practice, i.e., the field of implementation research.
The course uses real-life case studies and with a special emphasis on the under- and overuse of health care interventions in higher-income health systems. Individual sessions will cover the historical developments that have led to the current emphasis on implementation research, theories of individual and organizational behavior and behavior change, qualitative and mixed methods study designs, observational and experimental methods for implementation research and associated analytic techniques. Along with several of their classmates, students will design and develop a brief proposal to evaluate an evidence-based intervention addressing a significant gap in health or healthcare.
Research with Large Databases (HPM 299) provides an overview of existing large administrative, clinical, and national survey databases and addresses the potential uses of these data to study important questions regarding clinical risk factors, treatment, outcomes and health policy. Strengths and limitations of large databases that are commonly used for research will be considered. Special attention will be devoted to large federal databases that are publicly available and readily usable by new investigators. Students will have hands-on experience using SAS statistical software to obtain, create, manipulate, and analyze large databases. Key statistical issues, including risk-adjustment and sampling weights, will be emphasized in the course. Students will evaluate published studies based on large databases and develop a proposal for analyzing a specific research question with a large database. Note that this course uses SAS software (not STATA). Prior experience with SAS is not assumed or required.
Medical Informatics (HPM 512) and health information technology are increasingly critical for delivery of safe, effective health care, and also for research, and management. Health information technology is transforming health care, and electronic health records represent a treasure trove of data for anyone interested in clinical effectiveness research, and a vehicle for improving healthcare delivery. In this course we describe the core issues in the field of medical informatics, survey the methods used to perform clinical effectiveness research using clinical systems, give examples of healthcare improvement using health information technology, and describe how to evaluate clinical systems interventions. Major topics include: the impact of clinical systems with a focus on clinical decision support, evaluation methods, obtaining information from clinical systems, and the role of informatics standards. Issues such as confidentiality and privacy, organizational factors, interoperability, and return on investment will also be covered. So will the topics of social media, mobile health, big data, artificial intelligence and the cloud. The relevance of informatics in disease management, genomics, patient computing, biosurveillance, and health care policy will also be highlighted. You do not need to be a programmer or to have medical informatics as a primary interest to take this course.
Decision Analysis in Clinical Research (RDS 286) introduces students to systematic methods of decision analysis relevant to clinical decision making, clinical research and comparative effectiveness research. Topics of the sessions include: the use of causal estimands to express efficacy and real-world clinical effectiveness; the use of probability and sensitivity analysis to express and assess uncertainty; Bayes theorem and evaluation of diagnostic test strategies; utility theory and its use to express patient preferences for health outcomes; benefit-harm analysis and cost-effectiveness analysis in clinical research, clinical guideline development, health technology assessment and health policy decision making. Lectures are accompanied by case problems, review sessions and computer exercises. After this course, students will understand the uses, strengths, limitations and ethical issues of decision analysis and cost effectiveness in clinical decision making and research design. We will discuss case examples from different disease areas including cancer, cardiovascular disease, infectious disease and others.
Methods for Decision Making in Medicine (RDS 288) deals with intermediate-level topics in the field of medical decision making. Topics that will be addressed include building decision models, evaluation of diagnostic tests, utility assessment, multi-attribute utility theory, Markov cohort models, microsimulation state-transition models, calibration and validation of models, probabilistic sensitivity analysis, value of information analysis, and behavioral decision making. The course will focus on the practical application of techniques and will include published examples and a computer practicum. Students will learn to apply state-of-the-art modeling methods (using software packages) to evaluate the comparative effectiveness and cost-effectiveness of health interventions. While the primary emphasis is on application, essential underlying theoretical concepts will also be discussed. During the course you will have the opportunity to work on a decision problem which you select yourself. This is not an introductory course. Prerequisites are an introductory course in Decision Analysis (RDS280 or RDS286s or faculty approval of equivalent course) and knowledge of probability and statistics. The course has limited enrollment.
Collaborative Data Science in Healthcare (BST 209) The first two weeks of this course focus on methods for learning from data in order to gain useful predictions and insights. Through real-world examples of wide interest, we introduce methods for five key facets of an investigation: 1. data wrangling/cleaning in order to construct an informative, manageable data set; 2. software engineering skills for accessing data as well as organizing data analyses and making these analyses sharable and reproducible; 3. exploratory data analysis to generate hypotheses and intuition about the data; 4. inference and prediction based on statistical tools with a focus on machine learning; 5. communication of results through visualization, stories, and interpretable summaries.
During the last week of the course, with the help of the instructors and TAs, student teams will choose a clinically relevant question and complete a group project that includes parsing the question into a study design and methodology for data analysis and interpretation, with an emphasis on the data curation that is required before any analysis can be performed. The Medical Information Mart for Intensive Care (MIMIC) database and the eICU Collaborative Research Database will be used for each project. Students are expected to be familiar with R and RStudio before enrolling in this course.
Advanced Course Descriptions
Survival Methods in Clinical Research (BST 224) will cover statistical methods of survival analysis used in clinical research, including study design and power analysis, Kaplan-Meier product-limit estimation, Cox proportional hazards models, models with time-dependent covariates and repeated events, and models with competing risks. We will use SAS software in the course; however, students can use Stata, R, SPSS or other software. Students are encouraged to bring in their own project data for consultation. Course evaluation will be based on 13 daily quizzes.
Analytic Issues of Clinical Epidemiology (EPI 236) examines some features of study design but is primarily focused on analytic issues encountered in clinical research. These include techniques for stratified analysis, regression modeling, propensity scores, and matching. Emphasis is placed on the use of these techniques for the control of confounding and for the development of clinical prediction rules. The focus of this course is on applications and interpretations of results with limited introduction to theory that underlies these techniques. Course Activities include computer lab workshops that are scheduled during regular class time. Students must develop written summaries of the analyses of an assigned clinical data set from the results of daily computer workshops.