School of Public Health

Instructor: Ankur Pandya

Course Link: RDS 280 Decision Analysis for Health and Medical Practices

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. Prerequisites/Notes: Prerequisites include BIO 200 or BST 201 or BST 202 & 203 or BST 206 & 207 or BST 206 & 208 or BST 206 & 209 (all courses may be taken concurrently) or permission from the instructor. Introductory economics is recommended but not required. Students cannot take RDS 280 if they have already taken RDS 286 or RDS 202 (exceptions only allowed with permission of RDS 280 instructor).

Instructor: Stephen Resch

Course Link: RDS 282 Economic Evaluation of Health Policy & Program Management

Description: This course features 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: (1) theoretical foundations of cost-effectiveness analysis (CEA) with comparison to other methods of economic evaluation; (2) challenges and critiques of CEA in practice; (3) design and implementation of tools and protocols for measurement and valuation of cost and benefit of health programs; (4) use of evidence of economic value in strategic planning and resource allocation decisions, performance monitoring and program evaluation; (5) the role of evidence of economic value in the context of other stakeholder criteria and political motivations. Prerequisites/Notes: Students must have taken RDS 280 or RDS 286. Concurrent enrollment is allowed. Prior coursework in Microeconomics is recommended.

Instructor: James K. Hammitt

Course Link: RDS 284 Decision Theory

Description: This course introduces the standard model of decision-making under uncertainty, its conceptual foundations, challenges, alternatives, and methodological issues arising from the application of these techniques to health issues. Topics include von Neumann-Morgenstern and multi-attribute utility theory, Bayesian statistical decision theory, stochastic dominance, the value of information, judgment under uncertainty and alternative models of probability and decision making (regret theory, prospect theory, generalized expected utility). Applications are to preferences for health and aggregation of preferences over time and across individuals.

Instructor: Nicolas Menzies

Course Link: RDS 285 Decision Analysis Methods in Public Health and Medicine

Description: This intermediate-level course focuses on methods and health applications of 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. Prerequisites/Notes: Familiarity with matrix algebra and elementary calculus may be helpful but not required; lab or section times to be announced at first meeting. Prerequisites include (BST 201 or ID 201) and (RDS 280 or RDS 286). Concurrent enrollment is allowed for RDS 286.

Instructor: Ankur Pandya

Course Link: RDS 290 Experiential Learning and Applied Research in Decision Analysis

Description: This course is geared towards Masters-level students from any department. Students will undertake semester-long research projects on a clinical or public health decision problem using decision analysis, simulation modeling, and/or cost-effectiveness analysis. Each session will be dedicated to a particular topic of decision analytic methods or student presentations of prospectus, works-in-progress, and final projects. Students may work alone or in pairs, including at least one student who is familiar with the clinical content area of the project. Prerequisites/Notes: Prerequisites include (RDS 280 or RDS 286) and (RDS 285 or RDS 288).

Instructor: Uwe Siebert

Course Link: RDS 286 Decision Analysis in Clinical Research

Description: This course introduces students to systematic methods of decision analysis relevant to clinical decision making, clinical research, comparative effectiveness research and cost-effectiveness analysis. 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 procedures; utility theory and its use to express patient preferences for health outcomes; benefit-harm analysis for patient-shared decision making and clinical guideline development; cost-effectiveness analysis and health technology assessment for 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. Requires prior knowledge of clinical medicine (through training and/or clinical research experience) and strong quantitative ability/aptitude. Priority for enrollment will be given to students in the Program for Clinical Effectiveness (PCE). Prerequisites/Notes: Prerequisites include BST202 or BST206 (which may be taken concurrently) or BST201. Course restricted to HSPH Degree or PCE students. Any other interested students must request instructor permission to enroll in this course.

Instructors: Sue J. Goldie and Eve Wittenberg

Course Link: RDS 202 Decision Science for Public Health

Description: Challenges in public health policy and clinical medicine are marked by complexity, uncertainty, competing priorities and resource constraints. This course is designed to introduce the student to the methods and applications of decision analysis and cost-effectiveness analysis in clinical and public health decision making. The objectives of the course are: (1) to provide a basic introduction to the methods and tools of decision science, and to recognize when, how, and in what context they can provide value in clinical and public health decision making; (2) to equip students with the ability to structure and bound a decision problem logically (articulating the objective, perspective, and time horizon), identify key elements (alternatives, uncertainties, and outcomes) and influential factors (preferences, risk attitudes, values); (3) to provide students with basic skills in revising probabilities given new information, building and analyzing decision trees, conducting cost effectiveness analysis, performing sensitivity analyses, and communicating results; (4) to enable students to thoughtfully and critically evaluate published analyses conducted to evaluate or inform clinical strategies, health technologies, and public health policies in developed and developing countries. Prerequisites/Notes: Preference is given to students in the MPH-EPI and Summer-Only programs. However, all degree students are encouraged to participate.

Instructor: Zachary Ward

Course Link: RDS 203 Advanced Computational Methods for Disease Modelling

Description: This advanced course covers statistical and computational methods that are applicable for disease modelling in public health and medicine. Students will learn to apply state-of-the-art methods related to three core modules: 1) Numerical Methods, 2) Simulation-based Inference, and 3) High Performance Computing. This course is primarily intended for quantitative doctoral students (e.g., decision science, epidemiology, biostatistics), but is also open to advanced master’s students with an interest in computational science. Prerequisites/Notes: Course in mathematical modeling (RDS 285 or RDS 288), probability and statistics (BST201 or ID201 or (BST 206 and BST 207)), and basic knowledge of mathematical notation and reasoning. Prior programming experience (e.g., R, Python, C++, Java) is strongly recommended.

Instructor: David Macintosh

Course Link: RDS 500 Risk Assessment

Description: This course introduces the framework of risk assessment, considers its relationship with cost-benefit, decision analysis and other tools for improving environmental decisions. The scientific foundations for risk assessment (epidemiology, toxicology, and exposure assessment) are discussed. The mathematical sciences involved in developing models of dose-response, fate and transport, and the statistical aspects of parameter estimation and uncertainty analysis are introduced. Case studies are used to illustrate various issues in risk assessment and decision making. Course Activities: Lectures, discussions, case studies. Prerequisites/Notes: Course required for all Exposure, Epidemiology and Risk Program students.

Instructor: Joseph Pliskin

Course Link: HCM 732 Operations Management in Service Delivery Organizations

Description: Operations management is concerned with evaluating the performance of operating units, understanding why they perform as they do, designing new or improved operating procedures and systems for competitive advantage, making short-run and long-run decisions that affect operations, and managing the work force. To understand the role of operations in any organization, a manager must understand process analysis, capacity analysis, types of processes, productivity analysis, development and use of quality standards, and the role of operating strategy in corporate strategy. The course will also present the focused management approach which can help an organization achieve much more with existing resources. Prerequisites/Notes: Educational activity for a maximum of 27.5 AMA PRA Category 1 Credits TM. Physicians should only claim credit commensurate with the extent of their participation in the activity.