PhD in Biostatistics
Degree Description and Learner Objectives
The Ph.D. program will produce biostatisticians who can develop biostatistical methodology that can be utilized to solve problems in public health and the biomedical sciences. In addition, graduates of the Ph.D. program will be prepared to apply biostatistical and epidemiology methodology for the design and analysis of public health and biomedical research investigations. Finally, graduates of the Ph.D. program will be well suited to function as collaborators or team leaders on research projects in the biomedical and public health sciences.
The program requires competency in the theory of statistics and probability, in introductory and advanced biostatistical methods and theory, and in fundamentals of epidemiologic study design. The doctoral dissertation will be the culminating experience in the Ph.D. program. Graduates of the doctoral program will have written a doctoral dissertation which focuses on the development of a new methodology or on the innovative application of biostatistical methods to a health sciences research problem.
Graduates of the Ph.D. program will be in a position to:
- Demonstrate an increased level of knowledge and understanding of current statistical theory, methods, and practices in the health sciences
- Develop new statistical methods
- Design, manage data, analyze and interpret data from a variety of experimental and observational studies
- Communicate research findings, including new statistical methods developed, effectively to various audiences in writing and through oral presentation
The goals of the Ph.D. program are to train students in the application of appropriate statistical methods for diverse problems in medicine and public health, and to provide a solid theoretical foundation for the development and investigation of new statistical methods. In addition to the formal statistical training, students will have adequate flexibility in choosing statistical and non-statistical electives to tailor their curriculum towards a specific application area such as genetics, epidemiology, or environmental health.
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.
Prerequisites
The entrance requirements are the same as stated for the master’s degree. In addition, completion of an M.S. program in biostatistics or statistics, either at the University of Iowa or elsewhere, is generally required.
Course Requirements
M.S. Level Background: 33 s.h.
Ph.D. students must take the following 33 s.h. of Required Courses listed in the M.S. Program in Biostatistics:
CPH:6100, BIOS:5510, BIOS:5710, BIOS:5720, BIOS:5730, BIOS:6610, BIOS:7270, BIOS:7500, EPID:4400, and STAT:4100/4101 (or STAT:5100/5101).
(Students may request waivers and/or transfer of credit if they have already had the material at another institution. Course credits are automatically transferred for students who received their M.S. in Biostatistics from the University of Iowa.)
Core courses (17 semester hours required) (effective fall 2020)
| Number | Title | Semester | Hours |
|---|---|---|---|
| BIOS:6810 | Bayesian Methods and Design | (Fall even) | 3 s.h. |
| BIOS:7110 | Likelihood Theory and Extensions | (Fall) | 4 s.h. |
| BIOS:7210 | Survival Data Analysis | (Fall odd) | 3 s.h. |
| BIOS:7250 | Theory of Linear Models/Generalized Linear Models | (Spring) | 4 s.h. |
| BIOS:7310 | Longitudinal Data Analysis | (Spring 0dd) | 3 s.h. |
Doctoral students are required to earn a grade of at least a B- in each core course. If this requirement is not met, the core course must be repeated and a grade of at least B- must be achieved.
Electives and Dissertation (29 semester hours required)
With approval by a student’s academic advisor, students should choose 16-23 s.h. of courses from the following list. Other courses may count as electives, but require the approval of the advisor and the DGS. Independent Study (BIOS:7800) s.h. do not generally count as an elective and requires approval of the advisor and the DGS. At least 6 s.h. of electives need to be in courses taken for a letter grade.
Recommended, but not limited to, elective courses.
| Number | Title | Semester | Hours |
|---|---|---|---|
| BIOS:6420 | Survey Design and Analysis | (Spring even) | 3 s.h. |
| BIOS:6650 | Causal Inference | (Spring) | 3 s.h. |
| BIOS:6720 | Statistical Machine Learning for Biomedical and Public Health Data | (Spring even) | 3 s.h. |
| BIOS:7230 | Advanced Clinical Trials | (Fall even) | 3 s.h. |
| BIOS:7240 | High-Dimensional Data Analysis | (Spring odd) | 3.s.h. |
| BIOS:7330 | Advanced Biostatistical Computing | (Fall odd) | 3 s.h. |
| BIOS:7410 | Analysis of Categorical Data | (Spring even) | 3 s.h. |
| BIOS:7600 | Advanced Biostatistics Seminar (topics include model selection, spatial biostatistics, statistical methods in genetics/genomics, analysis of network data) | (arr) | 1-3 s.h. |
| BIOS:7850 | Research in Biostatistics | (arr) | 1-3 s.h. |
| STAT:6560 | Applied Time Series Analysis | (Spring) | 3 s.h. |
| STAT:7400 | Computer Intensive Statistics | (Spring) | 3 s.h. |
| BME:5335 | Computational Bioinformatics | (Spring) | 3 s.h. |
Dissertation Requirement
| Number | Title | Hours |
|---|---|---|
| BIOS:7900 | Dissertation (minimum of two semesters in residence) | 6-13 s.h. |
Total Semester Hours for Ph.D.: 79 s.h.
Ph.D. Comprehensive Examination
The Ph.D. comprehensive examination is offered once yearly. If the examination is not passed in the first attempt, it may be repeated one time. The examination consists of a two-day in-class component (two 3-hour examinations on consecutive days) and a take-home component. The in-class component contains a closed-book set of theory problems drawn from the Ph.D. core courses. The take-home component is comprised of three sections: data analytic problem, simulation problem and an open problem. In highly unusual circumstances, an oral examination may be given as a follow-up to the written examination if clarification is felt to be necessary by the departmental Comprehensive Examination Committee. Please refer to the Student Handbook for additional information regarding the Ph.D. comprehensive examination.
Ph.D. Dissertation Prospectus
The dissertation prospectus describes the rationale for the proposed research and outlines its basic components. Prior to initiation of the research, the prospectus is submitted to the student’s dissertation committee members. Please refer to the Dissertation Committee in the Student Handbook for the requirements of the Dissertation Committee membership. A meeting of the committee to evaluate the prospectus is required, and written approval by all committee members is required.
Dissertation Defense
The student and the student’s committee are required to comply with Graduate College guidelines with regard to preparation of the dissertation and meeting Graduate College deadlines for graduation. During the dissertation defense, the dissertation committee will thoroughly examine the student’s knowledge in the content area of the research.