Twin Cities campus
Twin Cities Campus

Biostatistics Ph.D.

School of Public Health - Adm
School of Public Health
Link to a list of faculty for this program.
Contact Information
School of Public Health, MMC 819, A395 Mayo Memorial Building, 420 Delaware Street, Minneapolis, MN 55455 (612-626-3500 OR 1-800-774-8636, Fax: 612-624-4498)
  • Program Type: Doctorate
  • Requirements for this program are current for Fall 2017
  • Length of program in credits: 59 to 67
  • This program does not require summer semesters for timely completion.
  • Degree: Doctor of Philosophy
Along with the program-specific requirements listed below, please read the General Information section of this website for requirements that apply to all major fields.
Biostatistics combines statistics, biomedical science, and computing to advance health research. Biostatisticians design, direct, and analyze clinical trials; develop new statistical methods; and analyze data from observational studies, laboratory experiments, and health surveys. This is an ideal field for students who have strong mathematical backgrounds and who enjoy working with computers, collaborating with investigators, and participating in health research. Students take courses in biostatistical methods, theory of statistics, clinical trials, statistical computing, categorical data, survival analysis, and health sciences.
Program Delivery
  • via classroom (the majority of instruction is face-to-face)
Prerequisites for Admission
The preferred undergraduate GPA for admittance to the program is 3.70.
Other requirements to be completed before admission:
At least three semesters of calculus (including multivariable) and one semester of linear algebra, and two semesters of undergraduate courses in probability and mathematical statistics are strongly recommended. Real analysis or an equivalent is recommended. Experience with programming language (e.g., R, Java, C) and exposure to applied statistics is helpful, but not required. In addition to completing the SOPHAS application, applicants must submit the following directly to SOPHAS: • Statement of purpose and objectives (an essay describing past education, experience, and current professional career objectives) • Résumé or curriculum vitae • Official postsecondary transcripts from all institutions attended, including previous study at the University of Minnesota (have transcripts sent directly from the institutions to SOPHAS) • Three letters of recommendation from persons qualified to assess academic work; clinical, public health, or professional experience; and leadership potential
Special Application Requirements:
All admitted international Ph.D. applicants are required to provide a World Education Services (WES) document verification report prior to beginning the program. Proof of English Proficiency Applicants whose native language is not English, or whose academic study was done exclusively at non-English speaking institutions, must prove English proficiency by providing either official Test of English as a Foreign Language (TOEFL) or International English Language Testing System (IELTS) scores. Official report of the scores should be sent directly to SOPHAS using designation code 5688 for the TOEFL or designation code SOPHAS for the IELTS. Scores must be less than two years old. The preferred minimum English language test scores for admission to the School of Public Health are listed below. The English Language test requirement may be waived if an applicant can provide proof of one of the following: - Completion of 16 semester credits/24 quarter credits (within the past 24 months) in an academic program at a recognized institution of higher learning in the U.S. or Canada. - An Educational Commission for Foreign Medical Graduates (ECFMG) certificate. Students should have an official or attested copy sent directly to the University of Minnesota School of Public Health at the address listed above.
Applicants must submit their test score(s) from the following:
  • GRE
    • General Test - Verbal Reasoning: 150
    • General Test - Quantitative Reasoning: 146
International applicants must submit score(s) from one of the following tests:
    • Internet Based - Total Score: 100
    • Paper Based - Total Score: 600
    • Total Score: 7.0
    • Final score: 80
Key to test abbreviations (GRE, TOEFL, IELTS, MELAB).
For an online application or for more information about graduate education admissions, see the General Information section of this website.
Program Requirements
35 to 43 credits are required in the major.
24 thesis credits are required.
This program may be completed with a minor.
Use of 4xxx courses towards program requirements is not permitted.
A minimum GPA of 3.30 is required for students to remain in good standing.
At least 3 semesters must be completed before filing a Degree Program Form.
The PhD program usually requires three years of full-time study after the MS degree. Students entering the PhD program without an MS degree in mathematics or statistics will be required to take additional core coursework.
Required Coursework
Core Coursework
All students take the following 20 credits of core coursework:
PUBH 8401 - Linear Models (4.0 cr)
PUBH 8403 - Research Skills in Biostatistics (1.0 cr)
PUBH 8412 - Advanced Statistical Inference (3.0 cr)
PUBH 8432 - Probability Models for Biostatistics (3.0 cr)
PUBH 8442 - Bayesian Decision Theory and Data Analysis (3.0 cr)
STAT 8101 - Theory of Statistics 1 (3.0 cr)
STAT 8102 - Theory of Statistics 2 (3.0 cr)
Elective Coursework
All students take at least 3 elective courses for a total of 9 or more credits from the following biostatistics and statistics course lists. Courses are selected in consultation with the advisor.
Biostatistics Elective Courses
PUBH 7420 - Clinical Trials: Design, Implementation, and Analysis (3.0 cr)
PUBH 7465 - Biostatistics Consulting (3.0 cr)
PUBH 8422 - Modern Nonparametrics (3.0 cr)
PUBH 8435 - Latent Variable Measurement Models and Path Analysis (3.0 cr)
PUBH 8445 - Statistics for Human Genetics and Molecular Biology (3.0 cr)
PUBH 8446 - Advanced Statistical Genetics and Genomics (3.0 cr)
PUBH 8452 - Advanced Longitudinal Data Analysis (3.0 cr)
PUBH 8462 - Advanced Survival Analysis (3.0 cr)
PUBH 8472 - Spatial Biostatistics (3.0 cr)
PUBH 8482 - Sequential and Adaptive Methods for Clinical Trials (3.0 cr)
PUBH 8492 - Theories of Hierarchical and Other Richly Parametrized Linear Models (3.0 cr)
Statistics Elective Course
Students may select, in consultation with the advisor, an 8xxx-level course offered by the School of Statistics that is not among the core courses listed above.
STAT 8xxx
Biostatistics Topics Course
Students may select, in consultation with the advisor, any PUBH 84xx biostatistics topic course that is not among the core courses listed above.
Health Science Elective
Take 3 credits of PUBH health science electives offered by other divisions in the School of Public Health or other Academic Health Center programs.
PUBH 6xxx
PUBH 7xxx
PUBH 8xxx
Survival Analysis Course
Take PUBH 7450 as early as possible during the PhD program. Students who have taken a course equivalent to PUBH 7450 should confer with their advisor regarding a substitute course.
PUBH 7450 - Survival Analysis (3.0 cr)
Thesis Credits
Take at least 24 doctoral thesis credits.
PUBH 8888 - Thesis Credit: Doctoral (1.0-24.0 cr)
Curriculum for students without an MS in mathematics or statistics
Students without the MS in mathematics or statistics must take two additional core courses. Students also are strongly recommended to gain more background in real analysis by taking MATH 4603, Advanced Calculus I, in the fall of their first year. Students with a prior analysis course may choose instead, but are not required, to take MATH 5615 and MATH 5616 as an elective.
Additional Core Coursework
In addition to the standard curriculum outlined above, take the following two courses:
PUBH 7405 - Biostatistics: Regression (4.0 cr)
PUBH 7406 - Advanced Regression and Design (4.0 cr)
More program views..
View college catalog(s):
· School of Public Health

View PDF Version:
Search Programs

Search University Catalogs
Related links.

School of Public Health

Graduate Admissions

Graduate School Fellowships

Graduate Assistantships

Colleges and Schools

One Stop
for tuition, course registration, financial aid, academic calendars, and more
PUBH 8401 - Linear Models
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Theory/application of statistical techniques for regression analysis. Computing for linear models. Modeling, computation, data analysis. prereq: [[7405, concurrent registration is required (or allowed) in STAT 8101] or instr consent], calculus, familiar wtih matrix/linear algebra
PUBH 8403 - Research Skills in Biostatistics
Credits: 1.0 [max 1.0]
Grading Basis: S-N only
Typically offered: Every Fall
Introduces research skills necessary for writing/defending dissertation, career in research. prereq: Stat 8101-02 and admission to PhD program in Biostatistics. The course is meant to be taken the fall before PhD written exam is attempted, so Schedule 2 students typically wait to enroll until second year in program.
PUBH 8412 - Advanced Statistical Inference
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Overview of inferential methods needed for biostatistical research. Topics without overt reliance on measure-theoretic concepts. Classic likelihood inference, asymptotic distribution theory, robust inferential methods (M-estimation). prereq: Stat 8101-8102 or equivalent, students should be comfortable with multivariate normal distribution/have some introduction to convergence concepts
PUBH 8432 - Probability Models for Biostatistics
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Three basic models used for stochastic processes in the biomedical sciences: point processes (emphasizes Poisson processes), Markov processes (emphasizes Markov chains), and Brownian motion. Probability structure and statistical inference studied for each process. prereq: [7450, 7407, Stat 5102, [advanced biostatstics or statistics] major] or instr consent
PUBH 8442 - Bayesian Decision Theory and Data Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Theory/application of Bayesian methods. Bayesian methods compared with traditional, frequentist methods. prereq: [[7460 or experience with FORTRAN or with [C, S+]], Stat 5101, Stat 5102, Stat 8311, grad student in [biostatistics or statistics]] or instr consent
STAT 8101 - Theory of Statistics 1
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Review of linear algebra. Introduction to probability theory. Random variables, their transformations/expectations. Standard distributions, including multivariate Normal distribution. Probability inequalities. Convergence concepts, including laws of large numbers, Central Limit Theorem. delta method. Sampling distributions. prereq: Statistics grad major or instr consent
STAT 8102 - Theory of Statistics 2
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Statistical inference. Sufficiency. Likelihood-based methods. Point estimation. Confidence intervals. Neyman Pearson hypothesis testing theory. Introduction to theory of linear models. prereq: 8101, Statistics graduate major or instr consent
PUBH 7420 - Clinical Trials: Design, Implementation, and Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Introduction to and methodology of randomized clinical trials. Design issues, sample size, operational details, interim monitoring, data analysis issues, overviews. prereq: 6451 or concurrent registration is required (or allowed) in 6451 or 7406 or instr consent
PUBH 7465 - Biostatistics Consulting
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Professional roles/responsibilities of practicing biostatistician as consultant/collaborator in health science research. Discussion, written assignments, student presentations, meeting notes, interviews, guests. prereq: [[[7405, 7406, 7407] or [STAT 8051, STAT 8052]], [[STAT 5101, STAT 5102] or [STAT 8101, STAT 8102]], biostatistics major] or instr consent
PUBH 8422 - Modern Nonparametrics
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Classical nonparametric inference, exact tests, and confidence intervals. Robust estimates. The jackknife. Bootstrap and cross-validation. Nonparametric smoothing and classification trees. Models/applications. Formal development sufficient for understanding statistical structures/properties. Substantial computing. prereq: [7406, STAT 5102, [public health or grad student]] or instr consent
PUBH 8435 - Latent Variable Measurement Models and Path Analysis
Credits: 3.0 [max 3.0]
Course Equivalencies: 01262
Typically offered: Every Fall
Introduction to use of statistical techniques known collectively as latent variable models. Exploratory/confirmatory factor analysis, path analysis, structural equation modeling, latent trait models, latent class models. SAS/AMOS software are used. prereq: Biostatistics PhD student or instr consent
PUBH 8445 - Statistics for Human Genetics and Molecular Biology
Credits: 3.0 [max 3.0]
Course Equivalencies: 01183
Typically offered: Every Spring
Introduction to statistical problems arising in molecular biology. Problems in physical mapping (radiation hybrid mapping, DDP), genetic mapping (pedigree analysis, lod scores, TDT), biopolymer sequence analysis (alignment, motif recognition), and micro array analysis. prereq: [[[Stat 8101, Stat 8102] or equiv], PhD student] or instr consent; some background with molecular biology desirable
PUBH 8446 - Advanced Statistical Genetics and Genomics
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Genetic mapping of complex traits in humans, modern population genetics with an emphasis on inference based observed molecular genetics data, association studies; statistical methods for low/high level analysis of genomic/proteomic data. Multiple comparison and gene network modeling. prereq: [7445, statistical theory at level of STAT 5101-2; college-level molecular genetics course is recommended] or instr consent
PUBH 8452 - Advanced Longitudinal Data Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Methods of inference for outcome variables measured repeatedly in time or space. Linear/nonlinear models with either normal or non-normal error structures. Random effects. Transitional/marginal models with biomedical applications. prereq: [Stat 5102, Stat 8311, experience with [SAS or S+], advanced [biostats or stat] student] or instr consent
PUBH 8462 - Advanced Survival Analysis
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Statistical methods for counting processes. Martingale theory (transforms, predictable processes, Doob decomposition, convergence, submartingales). Applications to nonparametric intensity estimation. Additive/relative risk models. Inference for event history data, recurrent events, multivariate survival, diagnostics. prereq: [7450, 8432, Stat 5102, advanced [biostatistics or statistics] major] or instr consent
PUBH 8472 - Spatial Biostatistics
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Spatial data, spatial statistical models, and spatial inference on unknown parameters or unobserved spatial data. Nature of spatial data. Special analysis tools that help to analyze such data. Theory/applications. prereq: [[STAT 5101, STAT 5102] or [STAT 8101, STAT 8102]], some experience with S-plus; STAT 8311 recommended
PUBH 8482 - Sequential and Adaptive Methods for Clinical Trials
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Statistical methods for design/analysis of sequential experiments. Wald theorems, stopping times, martingales, Brownian motion, dymamic programming. Compares Bayesian/fequentist approaches. Applications to interim monitoring of clinical trials, medical surveillance. prereq: Stat 8101-8102 or equivalent, [students should be comfortable with the multivariate normal distribution or instr consent]
PUBH 8492 - Theories of Hierarchical and Other Richly Parametrized Linear Models
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Spring Odd Year
Linear richly-parameterized models. Hierarchical/dynamic/linear/linear mixed models. Random regressions. Smoothers, longitudinal models. Schemes for specifying/fitting models. Theory/computing for mixed-linear-models. Richly parameterized models and the odd/surprising/undesirable results in applying them to data sets. Lectures, class project. prereq: [[8401 or STAT 8311], [[STAT 8101, STAT 8102] or equiv], [biostatistics or statistics] PhD student] or instr consent
PUBH 7450 - Survival Analysis
Credits: 3.0 [max 3.0]
Prerequisites: 7406, [STAT 5102 or STAT 8102]
Typically offered: Every Fall
Statistical methodologies in analysis of survival data. Kaplan-Meier estimator, Cox's proportional hazards multiple regression model, time-dependent covariates, analysis of residuals, multiple failure outcomes. Typical biomedical applications, including clinical trials and person-years data. prereq: 7406, [STAT 5102 or STAT 8102]
PUBH 8888 - Thesis Credit: Doctoral
Credits: 1.0 -24.0 [max 100.0]
Grading Basis: No Grade
Typically offered: Every Fall, Spring & Summer
(No description) prereq: Max 18 cr per semester or summer; 24 cr required; For Environmental Health Students ONLY: Contact Director of Graduate Studies and the Graduate Student Coordinator.
PUBH 7405 - Biostatistics: Regression
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
T-tests, confidence intervals, power, type I/II errors. Exploratory data analysis. Simple linear regression, regression in matrix notation, multiple regression, diagnostics. Ordinary least squares, violations, generalized least squares, nonlinear least squares regression. Introduction to General linear Model. SAS and S-Plus used. prereq: [[Stat 5101 or concurrent registration is required (or allowed) in Stat 5101], biostatistics major] or instr consent
PUBH 7406 - Advanced Regression and Design
Credits: 4.0 [max 4.0]
Prerequisites: [7405, [STAT 5102 or &STAT 5102], biostatistics major] or #
Typically offered: Every Spring
Topics include maximum likelihood estimation, single and multifactor analysis of variance, logistic regression, log-linear models, multinomial logit models, proportional odds models for ordinal data, gamma and inverse-Gaussian models, over-dispersion, analysis of deviance, model selection and criticism, model diagnostics, and an introduction to non-parametric regression methods. R is used. prereq: [7405, [STAT 5102 or concurrent registration is required (or allowed) in STAT 5102], biostatistics major] or instr consent