There are a number of required courses that students must successfully complete to receive a MS in Pharmaceutical Health Services Research (PHSR). Please review the descriptions below for more information about these courses.
PHSR 610-Pharmacy, Drugs, and the Health Care System (3 credits)
An examination of the principle components of the US health care system with special emphasis on their relationship to the provision of drugs and pharmacy services.
PHSR 631 Computing and Analytic Methods for Observational Studies (3 credits)
This course focuses on: 1) programming tools and techniques for analyzing observational data using SAS, STATA and R; 2) best practices for storing, manipulating, and analyzing large datasets used in health outcomes studies; 3) programming commonly-used in statistical regression models for observational, non-randomized studies.
PHSR 650-CER & Pharmacoeconomics I (3 credits)
This course is designed to familiarize the student with the economic structure, conduct and performance of the pharmaceutical industry. The course includes such topics as prices and profits in the industry, productivity, cost, economies of scale, innovation, economic effects of regulation, cost benefit and cost effectiveness of pharmaceuticals and efficiency of drug delivery systems. Prerequisite: one undergraduate economics course or permission of the instructor.
PHSR 701-Research Methods I (3 credits)
This course is designed to introduce the student to the concepts of scientific research in pharmaceutical health services research. Topics to be discussed include the scientific method and problem solving processes, social science measurement, and several specific methods of research. Corequisite: Introduction to Biostatistics.
PHSR 702-Research Methods II (3 credits)
This course is designed to give research tools to design studies in the impact of pharmaceutical (or other) interventions or policies in actual practice settings. Unlike clinical trials where subjects are randomized to treatment or placebo arms, health services researchers typically are forced to use non-experimental designs with secondary data. This course will take you through the pitfalls in such designs and show you how to deal with them. Prerequisite: Research Methods I and an Upper Level Graduate course in Multiple Regression.
PHSR 704-Pharmacoepidemiology (3 credits)
An Introduction to the field of pharmacoepidemiology that uses quantitative research methods to examine questions of benefit or risk in regard to the use of marketed medications. The course is intended to offer useful techniques to medical and health researchers who wish to assess the utilization, effectiveness and safety of marketed drug therapies. Prerequisite: Introduction to Biostatistics and Principles of Epidemiology.
PREV 600-Principles of Epidemiology (3 credits)
A comprehensive treatment of the concepts and methods of chronic disease epidemiology. Topics include the classification of statistical associations and the methods for distinguishing between causal and non-causal associations. Case-control, cohort and experimental studies are considered in some detail. The course involves the presentation by students of epidemiologic papers including those linking lung cancer to cigarette smoking. Corequisite/Prerequisite: PREV 620 or an Introduction to Biostatistics equivalent.
PREV 619-Introduction to SAS (1 credit)
Provides the student with comprehensive experience in the application of epidemiological and biostatistical methods available in the Statistical Analysis System (SAS). Hands-on experience in weekly workshops is gained by conducting analyses of existing data designed to answer a research question. Prerequisite: PREV 620 concurrently and PREV 720, or consent of the instructor.
PREV 620-Principles of Biostatistics (3 credits)
This course is designed to develop an understanding of statistical principles and methods as applied to human health and disease. Topics include: research design; descriptive statistics; probability; distribution models; binomial, Poisson and normal distributions; sampling theory and statistical inference. Prerequisite: Knowledge of college algebra required. Calculus recommended.