Core Courses

The two introductory courses in Clinical Epidemiology and Biostatistics are directed at clinical investigators and comprise the core of this program.  These courses are inter-related, present the students with exercises in “active learning,” and provide experience in many aspects of clinical research.  They meet daily during a seven-week period.

Introduction to Biostatistics (BIO206/207/208)
Introduction to Clinical Epidemiology (EPI208)

Elective Courses

All participants in the PCE also take two elective courses, each of which lasts for one-half of the summer session.  Elective courses include the following:

Linear and Longitudinal Regression (BIO501)
Effectiveness Research with Longitudinal Healthcare Databases (EPI253)
Ethical Basis of the Practice of Public Health (ID251)
Improvement in Quality in Health Care (HPM253)
Introduction to Methods and Applications in Health Services Research (HPM276)
Research with Large Databases (HPM299)
Medical Informatics (HPM512)
Measuring and Analyzing the Outcomes of Health Care (HPM530)
Decision Analysis in Clinical Research (RDS286)
Methods for Decision Making in Medicine (RDS288)

Advanced Courses

After 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.

Principles of Clinical Trials (BIO214)
Survival Methods in Clinical Research (BIO224)
Analytic Issues of Clinical Epidemiology (EPI236)

Summer Session in Public Health Courses

In addition to the PCE course offerings listed above. PCE students are also eligible to select from the Summer Session in Public Health Studies course offerings to fulfill the Program afternoon elective requirements.

Please note, course offerings are subject to change.

Core Course Descriptions

Introduction to Biostatistics (BIO206/207/208) 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 statistical program will be taught during classes and used to carry out analyses.

Introduction to Clinical Epidemiology (EPI208) covers core epidemiologic concepts and study designs from the perspective of clinical research. Topics include the design and analysis of cohort, case control, randomized controlled trials, time series and pseudoexperimental designs and quality improvement studies; minimization of bias; assessment of effect modification; and the identification and control of confounding. Other classes cover related topics such as test evaluation, screening for disease, measuring quality of life, assessing the reliability and validity of questionnaires, propensity scores, and prediction rules. One class 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 in 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.

Elective Course Descriptions

Linear and Longitudinal Regression (BIO501) 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 SAS statistical package.

Effectiveness Research with Longitudinal Healthcare Databases (EPI253) Large longitudinal healthcare databases 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 evaluate published database studies, complete programming exercises with statistical software and hands-on access to a large longitudinal database, and prepare a proposal for analyzing a specific research question using a large healthcare database.

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)

Ethical Basis of the Practice of Public Health (ID251) is intended to provide physicians and public health professionals with an understanding of the major ethical issues confronting health care delivery and public health practice. This course will provide familiarity with some of the fundamental ethical theories that have shaped our thinking about key public health issues. We will engage in lively discussions on challenging issues such as: mandatory vaccinations, paternalistic public health policies, rationing of health care resources, the use of quarantine, genetic screening, access and “rights” to health care and personal responsibility for health. Students will learn to analyze complex ethical problems, and to apply philosophical theories to produce well-reasoned policy recommendations. The course meets in two sections that cover identical material.

Improvement in Quality in Health Care (HPM253) is designed for practicing physicians 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 (HPM276) introduces students to the interdisciplinary field of health services research. The course uses theory, methodology, and applications in a highly interactive teaching approach. 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 racial, ethnic, 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.

Research with Large Databases (HPM299) provides an overview of existing large administrative, clinical, and 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, and 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. Prior experience with SAS is not assumed or required.

Medical Informatics (HPM512) 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 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.

Measuring and Analyzing the Outcomes of Health Care (HPM530) emphasizes introductory concepts, methods, and practical procedures for measuring and analyzing patients’ health status, quality of life, satisfaction and cost-effectiveness for health outcomes research. The course reviews the fundamentals of health outcomes research methods necessary for 1) demonstrating improvement in patient outcomes, 2) controlling costs and allocating resources, 3) implementing disease management programs and 4) making effective public health, health technology and clinical decisions. Statistical methods needed to evaluate and use scales and indices are also presented and discussed. The course would be useful to public health and clinical researchers who must critically review and utilize outcomes data for public health, health care and clinical decision-making. The course should enable students to 1) conceptually define the meaning and purpose of outcomes research, 2) understand the role of epidemiology, health economics and database and information technology in conducting outcomes research, 3) evaluate the usefulness and utility of outcomes measures, 4) recognize the different types of measures used in outcomes research, including clinical, health status, quality-of-life, work/role performance, health care utilization, and patient satisfaction, 5) adopt new methods for modeling patient responses, interpret the meaning of measurement concepts and obtain a basic appreciation of the statistical analyses appropriate for outcomes research, 6) locate available research-quality instruments for measuring health care outcomes in order to make informed choices among existing instruments and 7) interpret the results of health outcomes research. Knowledge of basic statistical concepts is recommended.

Decision Analysis in Clinical Research (RDS286) introduces the following topics: decision analysis methods relevant to clinical decision making, clinical research and comparative effectiveness research; the use of probability to express uncertainty; Bayes theorem and evaluation of diagnostic test strategies; sensitivity analysis; utility theory and its use to express patient preferences for health outcomes; cost-effectiveness analysis in clinical research and health technology assessment; and uses, limits and ethical issues of decision analysis and cost-effectiveness in clinical decision making and research design.

Methods for Decision Making in Medicine (RDS288) 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. 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

Advanced Course Descriptions

Principles of Clinical Trials (BIO214) is designed for individuals interested in the scientific and practical aspects of clinical trials. Topics include trial design (randomization, blinding, control groups, sample size calculation, superiority vs. noninferiority, parallel group trials, crossover trials, factorial trials, the protocol document), data monitoring (DSMBs, interim monitoring methods, adaptive designs), data analysis issues (subgroup analyses, benefit:risk analyses), and reporting trial results in the medical literature (e.g., CONSORT). Students design a clinical trial in their own field of interest, write a proposal, and critique recently published medical literature.

Survival Methods in Clinical Research (BIO224) is an introduction to the common approaches to the display and analysis of survival data, including Kaplan-Meier curves, log rank tests, and Cox proportional hazards regression. Computing, using SAS, will be an integral component of the course. Students are encouraged to bring a dataset to analyze.

Analytic Issues of Clinical Epidemiology (EPI236) 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.