The CER Initiative has identified existing individual courses and educational programs at HSPH that would be of high value for students interested in developing CER skills. First, we present a short list of selected core courses for CER training. Second, we offer a more extensive overview of the CER curriculum at HSPH by department and degree programs. The CER course list includes both required and recommended courses. The instructors and terms apply to the Academic Year 2012-13. More information on the courses can be found on the Registrar’s office webpage under the heading Courses and Schedules.
Selected Core Courses
The following listing includes selected core courses for CER training.
New HSPH elective course in Spring 2012: Methods for Comparative Effectiveness Research
Course Description: Comparative Effectiveness Research (CER) is designed to inform health-care decisions by providing evidence on the effectiveness, benefits, and harms of different treatment options. The evidence is generated from research studies that compare drugs, medical devices, tests, surgeries, or ways to deliver health care. This course will introduce students to statistical issues in the design and analysis of comparative effectiveness studies. Topics will range broadly and will include causal inference, decision analysis, multilevel models, chronic disease modeling and more. The format will be that of a reading group. After a few initial overview lectures, the group will identify a set of papers of interest, drawing both from the statistical and medical literature. Students will take turns being the primary reviewer for a paper, though in each session all students are expected to independently and proactively engage in a critical evaluation of current approaches and methodologies.
Instructors: Drs. James Robins and Giovanni Parmigiani
BIO 214: Principles of Clinical Trials
Course Description: Designed for individuals interested in the scientific, policy, and management aspects of clinical trials. Topics include types of clinical research, study design, treatment allocation, randomization and stratification, quality control, sample size requirements, patient consent, and interpretation of results. Students design a clinical investigation in their own field of interest, write a proposal for it, and critique recently published medical literature.
Instructor: James Ware
Term: Spring 1
BIO 249: Bayesian Methods in Biostatistics
Course Description: General principles of the Bayesian approach, prior distributions, hierarchial models and modeling techniques, approximate inference, Markov chainMonte Carlomethods, model assessment and comparison. Bayesian approaches to GLMMs, multiple testing, nonparametrics, clinical trails, survival analysis.
Instructor: Francesca Dominici
EPI 221: Pharmacoepidemiology
Course Description: Within the framework of formal epidemiologic analysis, this course covers inference about the effects of pharmaceuticals from case reports, case series, vital statistics and other registration schemes, cohort studies, and case-control studies. Decision-making with inadequate data is examined from the perspectives of manufacturers and of regulators. Students are graded on the basis of group projects. This course is intended primarily for students wishing to pursue a career in the pharmaceutical industry or in national regulatory bodies, but may have more general interest as an applied mid-level course with a heavy methodological emphasis. Course Activities: Written and oral group projects, individual class presentations, class discussion.
Instructor: Alexander Walker
Term: Fall 2
EPI 233: Research Synthesis and Meta-analysis
Course Description: Concerned with the explosion of biological data for etiologic inquiry and the use of existing data to inform public health decision making, the course focuses on research synthesis and meta-analysis. We will review the principles and methods for combining epidemiology studies and introduce how other types of scientific evidence, such as toxicology or mode-of-action data, can be incorporated using weight of- evidence analyses. This course will emphasize the use of critical reviews and meta-analysis to explore data and identify sources of variation among studies. Course Activities: Students will learn the principles of a systematic review, to use existing meta-analys is software to apply principles outlined in the course on example data sets, and, on a topic of their choice, to conduct a critical review or meta-analysis that appropriately weights effect estimates in each study, assesses uncertainty, and incorporates other kinds of scientific data in the overall analysis.
Instructor: Chung-Cheng Hsieh
EPI 271: Propensity Score Analysis
Course Description: This course introduces basic and advanced theory underlying propensity score analyses and provides practical insights into the conduct of studies employing the method. Course readings will include propensity score theory as well as applications. Lectures are complemented by computer lab sessions devoted to the mechanics of estimating and using the propensity score as a tool to control for confounding in observational research. Students should have knowledge in multivariable modeling approaches. A course project will involve the application of propensity scores to a data set. Course Activities: Lectures, readings, homeworks, computer labs, participation, project.
Instructor: Tobias Kurth
EPI 289: Models for Causal Inference
Course Description: EPI289 describes models for causal inference, their assumptions, and their practical application to epidemiologic data. The course covers propensity score methods, the parametric g-formula, inverse probability weighting of marginal structural models, g-estimation of nested structural models, and instrumental variable methods. The course also introduces models for causal inference in the presence of time-varying exposures, which will be extensively studied in EPI207. EPI289 is designed to be taken after EPI201/EPI202. The epidemiologic concepts and methods studied in EPI201/202 will be reformulated within a modeling framework in EPI289. Familiarity with the SAS language is strongly recommended.
Instructor: Miguel Hernan
Term: Spring 1
HPM 530: Measuring Health Outcomes
Course Description: This course 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
- demonstrating improvement in patient outcomes
- controlling costs and allocating resources
- implementing disease management programs and
- 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
- conceptually define the meaning and purpose of outcomes research
- understand the role of epidemiology, health economics and database and information technology in conducting outcomes research,
- evaluate the usefulness and utility of outcomes measures,
- 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,
- 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,
- locate available research-quality instruments for measuring health care outcomes in order to make informed choices among existing instruments and
- interpret the results of health outcomes research.
Instructor: Marcia Testa
RDS 280: Decision Analysis for Public Health and Medicine
Course Description: This course is designed to introduce the student to the methods and growing range of applications of decision analysis and cost-effectiveness analysis in health technology assessment, medical and public health decision making, and health resource allocation. The objectives of the course are: (1) to provide a basic technical understanding of the methods used, (2) to give the student an appreciation of the practical problems in applying these methods to the evaluation of clinical interventions and public health policies, and (3) to give the student an appreciation of the uses and limitations of these methods in decision making at the individual, organizational, and policy level both in developed and developing countries.
Instructor: Sue Goldie
Term: Fall 2
RDS 282: Economic Evaluation for Health Policy and Program Management
Course Description: This course features case studies in the application of health decision science to policymaking and program management at various levels of the health system. Both developed and developing country contexts will be covered. Topics include:  theoretical foundations of cost-effectiveness analysis (CEA);  controversies and limitations of CEA in practice;  design and implementation of tools and protocols for measurement and valuation of cost and benefit of health programs;  integration of evidence of economic value into strategic planning and resource allocation decisions, performance monitoring and program evaluation;  the role of evidence of economic value in the context of other stakeholder criteria and political motivations.
Instructor: Stephen Resch
Term: Spring 2
RDS 285: Decision Analysis Methods
Course Description: An intermediate-level course on methods and health applications of cost-effectiveness analysis and decision analysis modeling techniques. Topics include Markov models, microsimulation models, life expectancy estimation, cost estimation, deterministic and probabilistic sensitivity analysis, value of information analysis, and cost-effectiveness analysis.
Instructor: Jane Kim
Term: Spring 1
RDS 286: Decision Analysis in Clinical Research
Course Description: 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. Requires knowledge of clinical medicine, though training and/or clinical research experience. Strong quantitative ability/aptitude is also required. Priority for enrollment will be given to students in the Program for Clinical Effectiveness (PCE). HSPH degree candidates who are not in PCE must demonstrate knowledge of clinical medicine, though training and/or clinical research experience. (Others should consider taking RDS 280 as an alternative.) Non-degree students must provide evidence of both clinical training/ research experience and mathematical ability (e.g., grades in quantitative courses taken, test scores).
Instructor: Milton Weinstein
RDS 288: Decision making in Medicine
Course Description: This course 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, 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 and a computer practicum. During the course you will have the opportunity to work on a decision problem which you select yourself.
Instructor: Myriam Hunink
Curriculum Overview for CER Training
To provide a more extensive overview of CER curriculum we also list courses for CER training by department and degree programs. The courses are classified into five categories: (1) core courses (Core); (2) foundational courses (Fnd); (3) methods courses (Meth); (4) applied courses (Appl); and (5) advanced courses (Adv).
CER course list by department
CER course list by degree program