Additional Relevant Optional Courses Available to Scholars

These courses are offered at the Yale Schools of Public Health and Management.

  • HPM 570, Cost-effectiveness Analysis and Decision Making (Paltiel) This course introduces students to the methods of decision analysis and cost-effectiveness analysis in health-related technology assessment, resource allocation, and clinical decision-making. The course aims to develop technical competence in the methods used, practical skills in applying these tools to case-based studies of medical decisions and public health choices, and an appreciation of the uses and limitations of these methods at the levels of national policy, health care organizations, and individual patient care. 
  • BIS 628 Longitudinal Data Analysis (Lin) This course covers methods for analyzing data in which repeated measures have been obtained for individuals over time. Different methods are discussed to handle both continuous and discrete longitudinal response data. Some of the approaches covered include linear, nonlinear, and generalized mixed effects models, as well as generalized estimating equations. The course also covers exploratory methods, and approaches for handling missing data. Hierarchical generalized linear models are discussed and used in detail. Emphasis is placed on applying the methods, understanding underlying assumptions, and interpreting results. Both SAS and S-Plus are used throughout the course. 
  • MGT 611 Policy Modeling (Kaplan) Policy Modeling provides an operational framework for exploring the costs and benefits of public policy decisions. The techniques employed include "back of the envelope" probabilistic models, Markov processes, queuing theory, and linear/integer programming. With an eye towards making better decisions, these techniques are applied to various policy problems. In addition to lectures, assigned articles and texts, and short problem sets, students will be responsible for completing a take-home midterm exam and a number of cases. 
  • MGT 828 Creativity and Innovation (Feinstein) Creativity and innovation generate novel ideas, products, applications, strategies and solutions. In this course students explore different aspects of creativity and innovation that are important in business and in life, including being creative oneself, nurturing creativity in others, managing activities of innovation in organizations, recognizing valuable creative ideas and innovations when one comes across them, and appreciating the competitive dynamics associated with innovations. 
  • MGT 878 Decision analysis (Alizamir) When faced with a complex, uncertain problem, how does one make a good decision? Decision analysis provides a logical framework for structuring and evaluating a decision scenario, with the goal of obtaining clarity of action. This framework involves formulating creative alternatives, characterizing uncertain events, and incorporating decision makers’ values and preferences. This course introduces a set of tools for framing problems and performing logical analyses, and provides a foundation for decision-analytic modeling. Decision trees and influence diagrams are discussed, as well as assessing the value of information, performing sensitivity analysis, and incorporating risk preferences. 
  • MGT 978 Health Care Operations (Pinker) The healthcare delivery system is made up of many organizations, from large hospitals to small private practices. The operations of these organizations are complex, as they involve many highly trained professionals with a wide range of specializations, sophisticated and expensive technology and customers (patients) with diverse needs in an environment that is increasingly cost-sensitive. At the same time, quality is multidimensional and hard to measure. In this course we study concepts and tools that can increase the efficiency and quality of healthcare delivery. Topics include capacity planning, scheduling and process design in healthcare as well as quality management techniques. We will use quantitative analysis tools such as optimization with Excel solver and Monte Carlo simulation. 
  • ECON S131 Econometrics and Data Analysis I (Vytlacil) This course teaches how to evaluate quantitative information and how to use data to answer quantitative questions in the social sciences. Three areas are covered:
    1. Probability provides a foundation for modeling uncertainties, such as the uncertainties faced by financial investors, insurers, and individuals in everyday life. We will study the mechanics of probability and the use of probability to make judgments about uncertain events.
    2. Statistics provides techniques for interpreting data, such as the data a marketing department might have on consumer purchases. Statistical methods permit us to use small amounts of information to answer larger questions.
    3. Linear regression, is an area of statistics dedicated to estimating the relationships between two or more variables. 
  • BIS 540a Fundamentals of Clinical Trials (Makuch) This course addresses issues related to the design, conduct, and analysis of clinical trials. Topics include protocol development, examination and selection of appropriate experimental design, methods of randomization, sample size determination, appropriate methods of data analysis including time-to-event (possibly censored) data, and interim monitoring and ethical issues. Prerequisites: BIS 505a or equivalent and second-year status. 
  • BIS 646b Non-Parametric Statistical Methods and Their Applications (Zhang) Nonparametric statistical procedures including recursive partitioning techniques, splines, bootstrap, and other sample reuse methods are introduced. Some of the supporting theory for these methods is proven rigorously; some is described heuristically. Advantages and disadvantages of these methods are illustrated by medical and epidemiological studies. Students may be required to compare these methods with parametric methods when analyzing data sets. Familiarity with basic statistical theory and computer languages is assumed. 
  • CDE 505a/PSYC 657a Social and Behavioral Foundations of Health (White) This course is an introduction to social and behavioral science issues that influence patterns of health and health care delivery. The focus is on integration of biomedical, social, psychological, and behavioral factors that must be considered when public health initiatives are developed and implemented. This course emphasizes the integration of research from the social and behavioral sciences with epidemiology and biomedical sciences. 
  • CDE 543b Applied Analytic Methods in Epidemiology (Desai) This computer lab-based course provides students with a comprehensive overview of data management and analysis techniques. The SAS statistical software program is used. Students learn how to create and manipulate data sets and variables using SAS; identify appropriate statistical tests and modeling approaches to evaluate epidemiologic associations; and perform a broad array of univariate, bivariate, and multivariate analyses using SAS and interpret the results. 
  • DE 619a Advanced Epidemiologic Research Methods (Risch) This advanced course focuses on quantitative issues and techniques relevant to the design and analysis of observational epidemiologic studies. Starting with formal definitions of the commonly used epidemiologic parameters, and assuming a working knowledge of ANOVA and linear regression, the course covers analyses based on various related types of regression, e.g., logistic, Poisson, Cox, etc. The GLIM and PECAN computer programs are described and used throughout. Students analyze and discuss data sets of generally increasing complexity.