Applied Health Economics (AHE)

Introduces the theories that underpin health insurance products. Discusses the impact of government policies on health insurance products. Explains the principles of cost-effectiveness analysis. Reviews approaches to health insurance used around the world. Discusses the potential impact of health insurance reforms.

Presents descriptive and inferential statistics, including sampling and probability and hypothesis testing. Presents general approaches to ANOVA, Chi-Square, t-tests and linear regression. Students are introduced to the SAS learning environment.

Presents the basic concepts and the foundation of economic evaluation used to inform healthcare decision makers. Introduces cost-effectiveness and budget impact analysis with applied healthcare decision problems relevant to policy makers, providers, health care managers, and other healthcare stakeholders. Discusses approaches for handling uncertainty and risk adjustments, determining probability values, costs, other key model parameters, and the importance of perspective in health care decision making.

This course begins with an overview of the research process, research methods and health behavior theory and discusses how each step in the research process will determine which statistical methods will be needed to answer the research question. The remainder of the course will teach you how to analyze data using logistic regression, survival analysis, and longitudinal methods. The course also covers propensity scores and propensity score matching as a method of reducing selection bias in observational studies. The course concludes with student presentations of statistical analyses covered in the course and reflection on adequacy of the methods, data, and analytic strategy for answering their research question.

Focuses on concepts, theory, and methods for measuring subjective health outcomes including symptoms, functional status and well-being, health-related quality of life, and satisfaction with health. Explores appropriate tools for measuring subjective health outcomes and considers the importance of evaluating the measurement characteristics of these tools. Covers current requirements for Patient-Reported Outcomes endpoint in filings with US regulatory authorities, including conceptualization, validation and evidentiary standards.

This course enables students to conduct AHEOR using managed care claims data. The course will discuss topics such as how quality may be assessed through claims data, how claims may be used to guide budget impact modeling, and how claims may be used to generate novel scientific findings. Students will be familiarized with the limitations of claims-based data, and strategies for working to overcome them.

This course is designed to offer a critical overview of Health Technology Assessments (HTAs) and a practical guidance for optimal evidence generation and synthesis activities from an international perspective. This course discusses the fundamentals of HTAs around the globe and their interface with regulatory bodies, describes the preparation processes for a global and local dossier including HEOR evidence generation activities, and elaborates the decision-making criteria of HTAs. Throughout the course, the evolving nature of HTA assessments throughout the life cycle of a health technology is emphasized.

Presents methods and means to evaluate occurrence of disease and effects of interventions on disease incidence, prevalence and outcomes. Covers conduct and synthesis of prospective and retrospective research in generating estimates of the benefits and harms of different interventions and strategies to prevent, diagnose, treat and monitor health conditions. Considers the strengths and weaknesses of research designs and statistical approaches to evidence assessments. Includes methodological challenges in observational research such as bias (systematic error) and confounding (mixing of effects). Discusses adjustments to deal with limitations of evidence and analytical strategies. Explores practical applications of epidemiological practice in outcomes research.

Presents advanced methods for conducting observational studies using real-world data. Reviews contemporary usage of real-world databases, exposing students to data coding and classification systems, defining of study variables, and validation of codes in databases. Extensive attention given to techniques for mitigating bias and confounding including use of propensity-scores, disease risk scores, instrumental variables (IV), and interrupted time series designs. Students are expected to have an understanding of epidemiology principles, methods and knowledge of linear and logistic regression. Understanding survival analysis is a plus as some class readings involve time-to-event analysis. In addition, basic knowledge of SAS software will benefit students in some sections of the course.

Reviews the key concepts of health technology assessment with a focus on cost-effectiveness analysis. Introduces statistical methods to inform decision processes for health interventions, presenting both foundational and more complex decision analytical models. Addresses search strategies to identify the evidence models synthesize and describes tools for assessing the quality of evidence. Covers key components of economic evaluation for the development and running of Excel-based models representing disease and treatment patterns, stages of clinical trials, and the objectives of a model.

Implementation of a project demonstrating ability to manipulate and analyze data to address an AHEOR research question.

Presentation of a portfolio of experiences in the degree program to demonstrate proficiencies in AHEOR core competencies.