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)
  • Program Type: Doctorate
  • Requirements for this program are current for Fall 2022
  • Length of program in credits: 53 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.
The biostatistics PhD prepares graduates to conduct original research, collaborate and consult with biomedical researchers, implement and disseminate results of this research, and teach and mentor others in the field. The School of Public Health is accredited by the Council on Education for Public Health (CEPH).
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:
Applications are accepted for fall semester admission only. 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.
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 (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
29 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.
Students entering the program without a statistics or biostatistics master's degree may need to complete additional preparatory coursework in their first year, selected in consultation with the advisor and Biostatistics director of graduate studies upon admission. Students who have not taken a real analysis course may need to complete MATH 4603 Advanced Calculus. Those who have taken a real analysis course are strongly encouraged, but not required to, take MATH 5615H. Preparatory coursework cannot be applied toward degree requirements. Courses must be taken A-F, unless offered only S/N.
Biostatistics Core Requirements (19 credits)
PUBH 6250 - Foundations of Public Health (2.0 cr)
PUBH 7450 - Survival Analysis (3.0 cr)
PUBH 8401 - Linear Models (3.0 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)
Biostatistics Electives (9 credits)
Select at least 9 credits, in consultation with the advisor, from the following:
PUBH 7420 - Clinical Trials: Design, Implementation, and Analysis (3.0 cr)
PUBH 7465 - Biostatistics Consulting (2.0 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 8475 - Statistical Learning and Data Mining (3.0 cr)
PUBH 8482 - Sequential and Adaptive Methods for Clinical Trials (3.0 cr)
PUBH 8485 - Methods for Causal Inference (3.0 cr)
PUBH 8492 - Theories of Hierarchical and Other Richly Parametrized Linear Models (3.0 cr)
Health Science Elective (1 credit)
Take at least one credit offered by other School of Public Health divisions or Health Sciences programs. This course is chosen in consultation with the advisor.
PUBH 6xxx
PUBH 7xxx
PUBH 8xxx
Thesis Credits
Take at least 24 doctoral thesis credits.
PUBH 8888 - Thesis Credit: Doctoral (1.0-24.0 cr)
Requirements for students entering the PhD without a master's in statistics or biostatistics
Students entering the PhD without a master's in statistics or biostatistics must complete an additional 14 credits, selected in consultation with advisor.
Additional Biostatistics Coursework (14 credits)
Take the following courses:
STAT 8101 - Theory of Statistics 1 (3.0 cr)
STAT 8102 - Theory of Statistics 2 (3.0 cr)
PUBH 7405 - Biostatistical Inference I (4.0 cr)
PUBH 7406 - Biostatistical Inference II (3.0 cr)
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PUBH 6250 - Foundations of Public Health
Credits: 2.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
In this course we will examine values, contexts, principles, and frameworks of public health. We will provide an introduction to public health, consider the history of public health, social/political determinants, impact of health disparities on race, class and gender, moral and legal foundations, public health structures, historical trauma and cultural competence, health and human rights, advocacy and health equity, communication and financing, and the future of public health in the 21st century. Grounded in theory and concepts, we will incorporate core competencies and skills for public health professionals and will focus on developing problem solving and decision-making skills through critical analysis, reflection, case studies, readings, and paper assignments.
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 8401 - Linear Models
Credits: 3.0 [max 4.0]
Typically offered: Every Fall
This course is concerned with the theory and application of linear models. The first part of the course will focus on general linear model theory from a coordinate-free geometric approach. The second half of the course covers theory, applications and computing for linear models, and concentrates on modeling, computation and data analysis. It is intended as a core course for biostatistics PhD students and statistics PhD students. 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
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: 2.0 [max 3.0]
Typically offered: Every Fall & Spring
This course examines the professional roles, responsibilities and analytic skills of the practicing biostatistician as consultant and collaborator in health science research. The spectrum of roles will be explored through lecture, readings, discussion, written assignments, and participation in statistical consulting sessions with investigators at the University of Minnesota and in the community. prereq: PubH 7405-7406 (or Stat 8051-8052) and Stat 5101-5102 (or Stat 8101-8102); Biostatistics graduate student.
PUBH 8445 - Statistics for Human Genetics and Molecular Biology
Credits: 3.0 [max 3.0]
Course Equivalencies: PubH 7445/PubH 8445
Typically offered: Fall Odd Year
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 8475 - Statistical Learning and Data Mining
Credits: 3.0 [max 3.0]
Course Equivalencies: PubH 7475/PubH 8475/Stat 8056
Typically offered: Periodic Spring
Statistical techniques for extracting useful information from data. Linear discriminant analysis, tree-structured classifiers, feed-forward neural networks, support vector machines, other nonparametric methods, classifier ensembles (such as bagging/boosting), unsupervised learning. prereq: [[[6450, 6451, 6452] or STAT 5303 or equiv], [biostatistics or statistics PhD student]] or instr consent
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 8485 - Methods for Causal Inference
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Although most of statistical inference focuses on associational relationships among variables, in many biomedical and health sciences contexts the focus is on establishing the causal effect of an intervention or treatment. Drawing causal conclusions can be challenging, particularly in the context of observational data, as treatment assignment may be confounded. The first part of this course focuses on methods to establish the causal effect of a point exposure, i.e., situations in which treatment is given at a single point in time. Methods to estimate causal treatment effects will include outcome regression, propensity score methods (i.e., inverse weighting, matching), and doubly robust approaches. The second half of the course focuses on estimating the effect of a series of treatment decisions during the course of a chronic disease such as cancer, substance abuse, mental health disorders, etc. Methods to estimate these time-varying treatments include marginal structural models estimated by inverse probability weighting, structural nested models estimated by G-estimation, and the (parametric) G-computation algorithm. We will then turn our attention to estimating the optimal treatment sequence for a given subject, i.e., how to determine "the right treatment, for the right patient, at the right time," using dynamic marginal structural models and methods derived from reinforcement learning (e.g., Q-learning, A-learning) and classification problems (outcome weighted learning, C-learning). PubH 8485 is appropriate for PhD students in Biostatistics and Statistics. The homework and projects will focus more on the theoretical aspects of the methods to prepare students for methodological research in this area. PubH 7485 is appropriate for Masters students in Biostatistics and PhD students in other fields who wish to learn causal methods to apply them to topics in the health sciences. This course uses the statistical software of R, a freely available statistical software package, to implement many of the methods we discuss. However, most of the methods discussed in this course can be implemented in any statistical software (e.g., SAS, Stata, SPSS, etc.) and students will be free to use any software for homework assignments.
PUBH 8492 - Theories of Hierarchical and Other Richly Parametrized Linear Models
Credits: 3.0 [max 3.0]
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 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.
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 7405 - Biostatistical Inference I
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 - Biostatistical Inference II
Credits: 3.0 [max 4.0]
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