HPD Course Descriptions

This is an introductory doctoral level 3-credit seminar for all incoming PhD students in Population Health and Clinical Outcomes. This course will help students understand what earning a PhD entails, opportunities that exist after earning a PhD, typical PhD-level work activities, and beginning the process of academic writing. Students should already be thinking about what their dissertation will be about, and we will build off of that throughout the course. 3 credits.

This course introduces the design, conduct, and analysis of clinical trials. Topics include types of clinical trials, study design, treatment allocation, randomization and stratification, quality control, sample size requirements, patient consent, and interpretation of results. 3 credits.

This course will provide an overview of the field of clinical outcomes assessment. The specific topics covered include: risk factors identification, clinical outcomes selection, risk adjustment methods, patient safety monitoring, and provider-based quality improvement performance reporting. Students will be introduced to a broad range of clinical outcomes including (but not limited to) short-term mortality, treatment-related morbidity, health-related quality of life, condition-specific metrics, patient satisfaction, health plan member satisfaction, utility theory, and cost-effectiveness analysis. An emphasis will be placed in this course is placed on learning how clinical outcomes research can provide a data-driven approach to influence patient, provider, program, and policy decisions. 3 credits.

This course covers the theory and application of univariate and multivariable techniques appropriate for longitudinal data. Students will be exposed to both theory and application addressing repeated measures challenges. 3 credits.

Clinical outcomes research frequently involves the analysis of non experimental retrospective databases.  Such databases pose a number of statistical challenges, due to their non experimental design and various data limitations. This course will review and discuss multivariate methods in clinical outcomes research, focusing on specific issues involved in building and interpreting these models.  These issues include causal inference, selection bias, measurement error, missing data problems, multicollinearity, and serial correlation. Clinical outcomes and health services research studies will be reviewed and discussed to illustrate these statistical issues and how they have been addressed in published research.  Students will be asked to review and evaluate clinical outcomes and health services research papers, and present their reviews for discussion in class. 3 credits.

This course will introduce students to health services and clinical outcomes research methods and applications of these approaches.  The course will begin with an overview of key statistical methods, outcomes measurement issues, and methods for assessing the economic value of clinical treatments. The second part of the course will consider specific applications of health services and clinical outcomes research from a review and critique of published studies.  Students will present and critique these studies together with the instructor.  Specific areas of applications will include: Estimating the Production of Health; Hospital Volume and Clinical Outcomes; Estimating Clinical Outcomes with Patient-Level Data; Racial and Ethnic Disparities and Medical Treatments; Electronic Medical Records and Clinical Outcomes; Cost Effectiveness Applications. 3 credits.

This course will assist students in synthesizing basic public health knowledge through completion of a grant writing experience.  Students will be introduced to the process of writing grant proposals, developing budgets, professional networking, publishing in the scientific literature, and planning for their future careers as public health professionals and academics.  Students will also present their own individual research projects, write their own grant proposal, and do a career mapping exercise. 3 credits.