The Clinical Science Program's Biostatistics Track emphasizes statistical and mathematical analysis of biomedical data. Graduates will be able to design and analyze observational and experimental studies. Doctoral level biostatisticians are expected to have knowledge of a wide variety of analytic methods, a deep understanding of the rationale for the use of the methods, and the ability to either employ or develop new methods. Through a sequence of courses, students learn the theory and methods of biostatistics, epidemiology, environmental health, health services and social and behavioral science.
Health services research is a multidisciplinary field that uses administrative and observational data to examine population-based patterns in health care delivery, with particular focus on utilization, cost, and the outcomes of care. Comparative Effectiveness Research (CER) translation to Patient Centered Outcomes Research (PCOR) seeks to identify the right treatment, for the right patient, in the right setting. Health Informatics is the use of healthcare information in cultivating better inter-provider collaboration in patient care. The knowledge gleaned is also used in informing healthcare reform. Both fields are highly interdisciplinary, and includes aspects of information technology, biostatistics, and medicine. Both survival analysis and multilevel analysis are widely used in this field. To address selection bias in CER/PCOR, analytical methods such as natural experimental design, propensity scores, instrumental variables, and sensitivity analysis are used.
Students choosing to focus on health services research will learn how health and biomedical data are collected and processed into health information and knowledge and how they are applied to support clinical decision making. Our faculty has extensive experience analyzing a variety of data types, such as large survey data, Medicare, Medicaid, commercial insurance data, disease registry data and Electronic Medical Record data. Further, they have been involved in using such data to help guide public policy, investigate readmission rates, and examine how health care utilization varies or is influenced by factors such as ethnicity, income, education and marital status. Our faculty has received substantial federal funding in this area.