Search Results

BIOS 905. Theory of Statistical Inference. 3 Hours.

This course covers advanced aspects of statistical inference. It is aimed at preparing Ph.D. BIOS students for the Ph.D. comprehensive exam and will emphasize advanced biostatistical ideas as well as problem solving techniques. Prerequisite: Corequisite: BIOS 871 and BIOS 872 or equivalent and permission of instructor. LEC.

Doctor of Philosophy in Biostatistics

The Biostatistics M.S. and Ph.D. programs were created to help meet the ever-increasing demand for biostatisticians to take leadership roles in careers as researchers and educators in academia, government, and industry. Faculty members are active researchers collaborating and consulting in research projects and initiatives at the Medical Center, in addition to pursuing their own research agendas and participating in curricular instruction. Expertise in the Department includes linear, nonlinear, and longitudinal modeling; clinical trial and experimental design; survival analysis; categorical data analysis; robust statistics; psychometric methods; statistical 'omics; bioinformatics; Bayesian methodology; data science; and machine learning. The Ph.D. program produces biostatisticians who can develop biostatistical methodology that can be used to solve problems in public health and the biomedical sciences. In addition, graduates are prepared to apply biostatistical and epidemiology methodology for the design and analysis of public health and biomedical research investigations. Finally, graduates are well suited to function as collaborators or team leaders on research projects in the biomedical and public health sciences. In addition to the characteristics outlined in the M.S. program, graduates of the Ph.D. program in Biostatistics will have: the ability to develop careers in academia, research institutes, government, and industry; a broad understanding of current statistical methods and practices in the health sciences; a solid theoretical training necessary for the development and study of new statistical methods; the ability to assume all responsibilities of a statistician in collaborative health science research; in particular, the graduate will have experience in the design, data management, analysis, and interpretation of a variety of experimental and observational studies; experience in writing reports and giving oral presentations describing health science studies.