The doctoral program in Biostatistics trains future leaders, highly qualified as independent investigators and teachers, and who are well-trained practitioners of biostatistics. The program includes coursework in biostatistics, statistics, and one or more public health or biomedical fields. In addition, successful candidates are required to pass PhD applied and theory exams and write a dissertation that reports the results of new biostatistical research undertaken by the candidate.
Clinical medicine, epidemiologic studies, biological laboratory and field research, genetics, environmental health, health services, ecology, fisheries and wildlife biology, agriculture, and forestry.
Applicants usually have a degree in mathematics, statistics, or a biological field. All applicants should have the equivalent of 30 or more quarter credits in mathematics and statistics, including linear algebra, probability theory, and approximately 2 years of calculus.
Concurrent Option: PhD/MD
Application Deadline: Dec 1 - Autumn Quarter Entry
Upon satisfactory completion of the PhD in Biostatistics, graduates will be able to:
- Meet the learning objectives of the MS program in Biostatistics;
- Recommend and defend appropriate choices of methods to analyze independent outcome data;
- Implement non-standard statistical methods accurately and efficiently;
- Provide rigorous proofs characterizing the properties of standard statistical methods;
- Consult effectively with other scientists, addressing statistical issues in the design and analysis of public health or biomedical studies; and
- Design and carry out biostatistical research that will propose a new statistical method or will provide new information about the properties of existing methods.
Learning objectives for the PhD program in Biostatistics in the Generic Pathway: Upon satisfactory completion of the PhD program in Biostatistics in the Generic Pathway, graduates will be able to:
- Recommend and defend appropriate choices of methods to analyze longitudinal, clustered and other non-independent outcome data;
- Develop expertise in an area of biostatistical methodology; explain the strengths and weakness of different statistical methods in that area; and
- Explain both orally and in writing how advanced statistical methods work, assessing their strengths and limitations, and the place of particular methods in the larger statistical literature.