RDS280 Decision Analysis for Health and Medical Practices

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 care technology assessment, medical decision making, and health resource allocation. The objectives of the course are: (1) to provide a 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.

Course Note: Introductory course in probability and statistics required; BIO200, BIO201, or BIO203 may be taken concurrently; introductory economics is recommended but not required.

RDS282 Cost-Effectiveness and Cost-Benefit Analysis for Health Program Evaluation

This course is designed to provide an introduction to methods for economic evaluation of health and environmental programs, including theory and applications. Topics include theory of benefit-cost and of cost-effectiveness analysis, definition and methods for estimating costs, stated-preference and revealed-preference methods for valuing health and mortality risk, quality-adjusted life years.

Course Note: Introductory decision analysis (e.g. RDS280, HPM286) and economics (e.g. HPM205, HPM206) are recommended.

RDS284 Decision Theory

Decision Theory 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 (Dempster-Shafer theory, generalized 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.

Course Note: Prior course work in decision analysis required.

RDS285 Decision Analysis Methods in Public Health and Medicine

RDS 285 is an intermediate-level course on methods and health applications of decision analysis and other modeling techniques. Topics include Markov models, life expectancy modeling, simulation models, deterministic and probabilistic sensitivity analysis, ROC analysis and diagnostic technology assessment, and cost-effectiveness analysis.
Course Note: RDS 280, RDS 286, or equivalent introductory course on decision analysis required or signature of instructor required; familiarity with matrix algebra and elementary calculus may be helpful but not required.

RDS286 Decision Analysis in Clinical Research

Decision Analysis in Clinical Research introduces such topics as: 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.

RDS288 Methods for Decision Making in Medicine

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. This is not an introductory course.
Course Note: RDS280 or RDS286 and some knowledge of probability and statistics required.