Core Courses The two introductory courses in Clinical Biostatistics and Epidemiology 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 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 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 and quasi experimental studies; minimization of bias; assessment of effect modification; and the identification and control of confounding. Other classes cover related topics such as test evaluation, 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 Courses All participants in the Program in Clinical Effectiveness also take two elective courses, each of which lasts for one-half of the summer session. These half-summer courses include, Current Issues in Health Policy, Decision Analysis in Clinical Research , Ethical Basis of the Practice of Public Health, Improvement in Quality in Health Care, Introduction to Methods and Applications in Health Services Research, Linear and Longitudinal Regression, Measuring and Analyzing the Outcomes of Health Care, Medical Informatics, Methods for Decision Making in Medicine, and Research with Large Databases.
Current Issues in Health Policy introduces students to the major health policy issues facing the United States today. The course focuses on the roles of hospitals, doctors, private and government insurance, and different systems for organizing and financing care (such as traditional fee-for-services, HMOs, and other forms of "managed care"). Individual sessions in the course will be devoted to topics such as malpractice, cost control, the impact of public opinion, policy issues related to pharmacologic therapy, physician payment, academic health centers, workforce, physician profiling, managed care, the uninsured, Medicare, and Medicaid.
Taken together, the course sessions are designed to provide both a general background of the health care system and knowledge of many of the cutting-edge issues that are on the forefront of the nation's health policy agenda. The course will provide insight into how and why particular health policies are developed. It will focus on what the major policy questions are, and present examples of health services research methodology. However, the course concentrates much more on policy questions than methodological techniques.
Decision Analysis in Clinical Research introduces the following topics: decision analysis methods relevant to clinical decision making and clinical 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 policy; and uses and limits of decision analysis and cost-effectiveness in clinical decision making and research design.
Ethical Basis of the Practice of Public Health is intended to provide physicians and public health professionals with an understanding of some of the major ethical issues confronting health care delivery and public health practice today and familiarity with some of the moral philosophical ideas that have shaped our thinking about them. Topics include rationing of health care resources, genetic screening, access and “rights” to health care, confidentiality, informed consent, research ethics, “fetal abuse,” and personal responsibility for health. Students will learn to analyze complex ethical problems and apply philosophical principles and theories to reach ethical conclusions and craft policy recommendations. The course meets intwo sections that cover identical material.
Improvement in Quality in Health Care 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 methods across organizational silos and boundaries; and work-related psychology and managing resistance to improvement.
Introduction to Methods and Applications in Health Services Research introduces students to the interdisciplinary field of health services research. The course covers theory, methodology, and applications using a highly interactive teaching approach. Individual sessions will be devoted to research design, understanding analyses of large databases, cost-effectiveness analysis, survey methodology, assessment of health status, assessment of quality, 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.
Linear and Longitudinal Regression 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.
Measuring and Analyzing the Outcomes of Health Care 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.
Medical Informatics and health information technology are increasingly critical for delivery of safe, effective health care, and also for research, and management. Health information technology will likely transform health care in the coming years, 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. 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.
Methods for Decision Making in Medicine deals with intermediate-level topics in the field of medical decision making. Topics that will be addressed include modeling issues, evaluation of diagnostic tests, ROC and summary ROC analysis, utility assessment, multi-attribute utility theory, Markov process models, Monte Carlo simulation modeling, retrieving and applying data, methods for 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. It also includes a computer practicum and you will work on a case problem selected by yourself. This is an intermediate level course and requires some knowledge about decision analysis eg. an introductory course.
Research with Large Databases addresses potential applications of existing large databases to study important questions regarding clinical risk factors, treatments, outcomes and health policy. Strengths and limitations of large databases that are commonly used for research will be considered, including data from Medicare, Medicaid, the Veterans Administration, NIH-funded cohort studies, and state and regional cancer registries. Special attention will be devoted to large federal databases that are readily available to new investigators, such as the Nationwide Inpatient Sample, Kid’s Inpatient Database, SEER-Medicare Database, National Health Interview Survey, Behavioral Risk Factor Surveillance System, National Health and Nutrition Examination Survey, US Renal Data System, and Medicare Current Beneficiary Survey. Practical issues in obtaining, linking, and analyzing large databases will be emphasized throughout the course, and key statistical issues will be addressed, including sampling weights and risk-adjustment. Students will discuss published studies based on large databases and develop a proposal for obtaining a large database and analyzing a specific research question with it. Prior experience with SAS statistical software is recommended though not required, and computer lab sessions will provide students with experience analyzing national databases using SAS and SUDAAN software.
Advanced Courses Qualified students who successfully complete the core courses in the Program in Clinical Effectiveness and other students with similar backgrounds are eligible to take second-level courses in Analytic Issues of Clinical Epidemiology, Principles of Clinical Trials, and Survival Methods in Clinical Research. These half-summer courses are offered at the same time as the first-year core courses. Students can take these courses in a second summer period or over two subsequent summers.
Analytic Issues of Clinical Epidemiology 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.
Principles of Clinical Trials 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 investigation in their own field of interest, write a proposal, and critique recently published medical literature.
Survival Methods in Clinical Research will cover 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.
Workload
Participants must be free of all clinical responsibilities. In addition to three classes daily, participants may have computer labs on some afternoons, and have considerable homework; 50% of last summer's participants spent, on average, over 20 hours each week on homework assignments.
