Doctoral Biostatistics

Overview

The Biostatistics doctoral program builds on the classic public health biostatistics skill and knowledge base and takes advantage of special knowledge of its faculty in the areas of genetics, bioinformatics and big data science. Students have the opportunity to learn and apply statistical genetics in the context of complex disease study, high-throughput computing used in “big data” science and applications in evidence-based patient-centered outcome studies. Courses include topics and material such as interpretation of personalized and evidence-based medicine in the context of public health; basic understanding of genetics and epigenetics; and general “omic” approaches and concepts.

Faculty expertise in: Genetic determinants of common chronic diseases (including heart disease, bleeding disorders, Type II diabetes, stroke and colorectal cancer); using genomic technologies and bioinformatic and biostatistical techniques to accurately predict risk and treatment response in cancer and cardiovascular disease; major molecular mechanisms and pathways that modulate disease progression; using biomedical informatics, mathematical modeling and simulations to characterize and predict the use of genetics in medical practice and in particular pathology; use of high-throughput genetic technologies such as micro-arrays and next generation sequencers in the discovery and applications of genetics to complex diseases and environmental-gene development pathways; and statistical methods and computational tools to identify genetic variants that influence the susceptibility to complex diseases such as cancer of the breast, colon/rectum, lung and prostate.

Information Sessions

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Application

The deadline to apply for the Biostatistics PhD for Fall 2017 is January 6, 2017. When applying for admission, students should describe their interest in the field and any research preferences. Applicants are encouraged to review the research interests of the faculty and contact those faculty who are of interest. All applicants must complete the required application through the School of Public Health Application System (SOPHAS).

Applicants to the PhD program in Public Health with a concentration in Biostatistics should have completed academic programs that facilitated development of solid analytical skills. Applicable baccalaureate programs include mathematics, statistics, computer science, and engineering. Baccalaureate degrees in related fields will be considered. A minimum undergraduate grade point average of 3.00 (A=4.00) is required. Applicants from diverse backgrounds are encouraged to apply. Each application will be evaluated individually primarily on the basis of academic achievement, although relevant work experience will also be considered.

Graduate Record Examination (GRE) scores from the general test (verbal, quantitative, analytical writing) are required of all applicants. Submitted test scores must be from a test taken within 5 years of the date of application. While there is not a minimum GRE score requirement, strong quantitative, verbal and writing skills are critical to successfully completing the program.

Applicants may request a GRE waiver or substitution in the following situations:

  • Holding a terminal degree (e.g., PhD, JD, PharmD, MD); please note that terminal degrees from foreign institutions must be post-baccalaureate
  • Completion of an alternative standardized test (e.g., MCAT, LSAT, GMAT)

Note that a waiver must be requested and granted by admissions staff at the Zilber School of Public Health. Contact applyph@uwm.edu regarding GRE waivers and substitutions.

Students must meet UWM Graduate School admission requirements. For international applicants whose native language is not English, the UW-Milwaukee Center for International Education web site provides English Language Proficiency Requirements including required TOEFL or IELTS scores, and students who attended an international university must also pay an additional fee for evaluation of international transcripts.

In addition, a personal statement, and at least three letters of recommendation from individuals familiar with the applicant’s scholarship, research achievements, and/or academic potential are required for the application. The letters of recommendation should address the candidate’s potential for achievement in a graduate program from an academic as well as personal (e.g., commitment, integrity, ethical) standpoint. At least one letter must be from a university faculty member.

A select group of the most qualified candidates will be invited to participate in an interview process. In-person or internet-facilitated interviews (Skype, etc.) will be required for finalist candidates prior to admission.

Applicants may be admitted with course deficiencies at the discretion of the ZSPH Graduate Program Committee. The student is expected to rectify these course deficiencies with a grade of B or better within three enrolled semesters. The academic program unit will monitor deficiencies. No course credits earned in making up deficiencies may be counted as program credits required for the degree. For students entering with an advanced degree, the Admissions Committee can grant credit for relevant coursework at its discretion, but at least half of the graduate credits required for the Ph.D. must be completed at UW-Milwaukee in doctoral status in accordance with Graduate School policy. Thesis, dissertation, and research credits must be completed at UW-Milwaukee.

Curriculum

Minimum degree requirement is 60 graduate credits beyond the bachelor’s degree (plus an additional 9 credits dedicated toward dissertation writing and research), at least 35 of which must be earned in residence at UWM. The student, in consultation with the major professor, must create a plan of study and submit to the Biostatistics Faculty by the end of the first year. Minimum course requirements for all work requires approximately two to three full years of study.

Credits and Courses

Required Core Ph.D. Courses, 12 credits

PH 702 Introduction to Biostatistics*, 3 cr
PH 704 Principles and Methods of Epidemiology, 3 cr
PH 801 Seminar in Public Health Research, 3 cr
PH 819 Social and Environmental Justice in Public Health, 3 cr

** It is expected that PH 702 will be waived for the majority of PhD students based on prior training, and an additional elective will be substituted

Required Methods Courses, 27 credits

MthStat 761 Mathematics Statistics, 3 cr
MthStat 762 Mathematical Statistics, 3 cr
Math 571 or Math 771 Introduction to Probability Models or Theory of Probability, 3 cr
PH 711 Intermediate Biostatistics, 3 cr
PH 713 Analyzing Observational and Experimental Data, 3 cr
PH 718 Data Management and Visualization in R, 3 cr
PH 813 Practice of Biostatistical Consulting, 3 cr
PH 818 Statistical Computing, 3 cr
PH 911Generalized Linear Models, 3 cr
PH 990 Research and Dissertation, 3cr

Electives, at least 21 credits

Doctoral Thesis, at least 9 credits

PH 990 Research and Dissertation, 3cr repeatable

 

Primary Faculty

Professors:
Tonellato, Peter, Ph.D., University of Arizona

Associate Professors:
Huang, Chiang-Ching, Ph.D., University of Michigan

Assistant Professors:
Auer, Paul, Ph.D., Purdue University
Carnegie, Nicole Bohme, Ph.D., University of Washington
Wang, Xuexia (Helen), Ph.D., Michigan Technological University
Zheng, Cheng, Ph.D., University of Washington

 

PhD in Public Health Competencies

Upon graduation, a student completing the requirements for the Ph.D. in Public Health with a concentration in Biostatistics will be able to:

  1. Develop new statistical methodologies to solve problems in biomedical, clinical, public health, or other fields
  2. Contribute to the body of knowledge in the field of biostatistics by writing and successfully submitting manuscripts for publication in a peer-reviewed journal
  3. Perform all responsibilities of a statistician in collaborative research; in particular: design studies, manage and analyze data and interpret findings from a variety of biomedical, clinical or public health experimental and observational studies
  4. Communicate statistical information effectively with individuals with varying degrees of statistical knowledge through written and oral presentations
  5. Use statistical, bioinformatic and other computing software to organize, analyze, and visualize data
  6. Review and critique statistical methods and interpretation of results in published research studies, presentations, or reports
  7. Demonstrate solid theoretical knowledge necessary for the development and study of new statistical methods.
  8. Understand and implement modern statistical approaches emerging in the literature to improve biomedical and public health.