Twin Cities campus
 
Twin Cities Campus

Biostatistics Minor

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: Graduate minor related to major
  • Requirements for this program are current for Spring 2018
  • Length of program in credits (master's): 6
  • Length of program in credits (doctoral): 12 to 14
  • This program does not require summer semesters for timely completion.
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. Minors are available for both University of Minnesota masters and doctoral level students.
Program Delivery
  • via classroom (the majority of instruction is face-to-face)
Prerequisites for Admission
Other requirements to be completed before admission:
Admission to the biostatistics graduate minor is contingent upon prior admission to a master's or doctoral degree-granting program. Students should first consult with their major program adviser about the advisability of a minor in biostatistics. Students will then need to contact the director of graduate studies (bstdgs@umn.edu). A biostatistics faculty member must be on the student's doctoral preliminary oral examination committee as well as masters and doctoral final oral examination committees.
For an online application or for more information about graduate education admissions, see the General Information section of this website.
Program Requirements
Use of 4xxx courses towards program requirements is not permitted.
Program Sub-plans
Students are required to complete one of the following sub-plans.
Students may not complete the program with more than one sub-plan.
Masters
Master's-level minor in Biostatistics
NOTE: One course may be taken S/N and all other courses must be taken A/F
Take 2 or more course(s) from the following:
· PUBH 7430 - Statistical Methods for Correlated Data (3.0 cr)
· PUBH 7435 - Latent Variable Measurement Models and Path Analysis (3.0 cr)
· PUBH 7440 - Introduction to Bayesian Analysis (3.0 cr)
· PUBH 7445 - Statistics for Human Genetics and Molecular Biology (3.0 cr)
· PUBH 7450 - Survival Analysis (3.0 cr)
· PUBH 7470 - Statistics for Translational and Clinical Research (3.0 cr)
· PUBH 7475 - Statistical Learning and Data Mining (3.0 cr)
· PUBH 7415 - Introduction to Clinical Trials (3.0 cr)
or PUBH 7420 - Clinical Trials: Design, Implementation, and Analysis (3.0 cr)
Doctoral
Doctoral Minor
14 credits are required for doctoral minor for non-statistics students. 12 credits required for doctoral minor for statistics students.
Doctoral-level minor in Biostatistics for Non-Statistics Students
Students should take the required set of two core courses (either 7405 and 7406, or 7401 and 7402) first, before choosing two additional courses from the list of elective courses below. NOTE: One course may be taken S/N and all other courses must be taken A/F
Biostatistics Core
Biostat Core Option 1
PUBH 7405 - Biostatistics: Regression (4.0 cr)
PUBH 7406 - Advanced Regression and Design (4.0 cr)
or Biostat Core Option 2
PUBH 7401 - Fundamentals of Biostatistical Inference (4.0 cr)
PUBH 7402 - Biostatistics Modeling and Methods (4.0 cr)
Electives
Take 2 or more course(s) totaling 6 or more credit(s) from the following:
· PUBH 7430 - Statistical Methods for Correlated Data (3.0 cr)
· PUBH 7435 - Latent Variable Measurement Models and Path Analysis (3.0 cr)
· PUBH 7440 - Introduction to Bayesian Analysis (3.0 cr)
· PUBH 7445 - Statistics for Human Genetics and Molecular Biology (3.0 cr)
· PUBH 7450 - Survival Analysis (3.0 cr)
· PUBH 7470 - Statistics for Translational and Clinical Research (3.0 cr)
· PUBH 7475 - Statistical Learning and Data Mining (3.0 cr)
· PUBH 7415 - Introduction to Clinical Trials (3.0 cr)
or PUBH 7420 - Clinical Trials: Design, Implementation, and Analysis (3.0 cr)
-OR-
Doctoral-level minor Biostatistics for Statistics Students
NOTE: One course may be taken S/N and all other courses must be taken A/F
PUBH 7420 - Clinical Trials: Design, Implementation, and Analysis (3.0 cr)
PUBH 7450 - Survival Analysis (3.0 cr)
Take 2 or more course(s) totaling 6 or more credit(s) from the following:
· PUBH 8422 - Modern Nonparametrics (3.0 cr)
· PUBH 8442 - Bayesian Decision Theory and Data Analysis (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)
 
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PUBH 7430 - Statistical Methods for Correlated Data
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Correlated data arise in many situations, particularly when observations are made over time and space or on individuals who share certain underlying characteristics. This course covers techniques for exploring and describing correlated data, along with statistical methods for estimating population parameters (mostly means) from these data. The focus will be primarily on generalized linear models (both with and without random effects) for normally and non-normally distributed data. Wherever possible, techniques will be illustrated using real-world examples. Computing will be done using R and SAS.
PUBH 7435 - Latent Variable Measurement Models and Path Analysis
Credits: 3.0 [max 3.0]
Course Equivalencies: 01262 - PubH 7435/PubH 8435
Typically offered: Every Fall
Introduction to use of latent variable models. Exploratory/confirmatory factor analysis, path analysis, structural equation modeling, latent trait models, latent class models. Uses SAS/AMOS software. prereq: [6414, 6415] or [6450, 6451] or biostatistics major or instr consent
PUBH 7440 - Introduction to Bayesian Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Introduction to Bayesian methods. Comparison with traditional frequentist methods. Emphasizes data analysis via modern computing methods: Gibbs sampler, WinBUGS software package. prereq: [[7401 or STAT 5101 or equiv], [public health MPH or biostatistics or statistics] grad student] or instr consent
PUBH 7445 - Statistics for Human Genetics and Molecular Biology
Credits: 3.0 [max 3.0]
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: [6450, [6451 or equiv]] or instr consent; background in molecular biology recommended
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 7470 - Statistics for Translational and Clinical Research
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Diagonostic medicine, including methods for ROC curve. Bioassays. Early-phase clinical trials, methods including dose escalation, toxicity, and monitoring. Qualitiy of life. prereq: [[6450, 6451] or equiv], [grad student in biostatistics or statistics or clinical research], familiarity with SAS
PUBH 7475 - Statistical Learning and Data Mining
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Various statistical techniques for extracting useful information (i.e., learning) from data. Linear discriminant analysis, tree-structured classifiers, feed-forward neural networks, support vector machines, other nonparametric methods, classifier ensembles, unsupervised learning. prereq: [[[6450, 6452] or equiv], programming backgroud in [FORTRAN or C/C++ or JAVA or Splus/R]] or instr consent; 2nd yr MS recommended
PUBH 7415 - Introduction to Clinical Trials
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Summer
Hypotheses/endpoints, choice of intervention/control, ethical considerations, blinding/randomization, data collection/monitoring, sample size, analysis, writing. Protocol development, group discussions. prereq: 6414 or 6450 or one semester graduate-level introductory 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 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
PUBH 7401 - Fundamentals of Biostatistical Inference
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Part of two-course sequence intended for PhD students in School of Public Health who need rigorous approach to probability/statistics/statistical inference with applications to research in public health. prereq: Background in calculus; intended for PhD students in public hlth and other hlth sci who need rigorous approach to probability/statistics and statistical inference with applications to research in public hlth
PUBH 7402 - Biostatistics Modeling and Methods
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Second of two-course sequence. Rigorous approach to probability/statistics, statistical inference. Applications to research in public health. prereq: 7401; intended for PhD students in health sciences
PUBH 7430 - Statistical Methods for Correlated Data
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Correlated data arise in many situations, particularly when observations are made over time and space or on individuals who share certain underlying characteristics. This course covers techniques for exploring and describing correlated data, along with statistical methods for estimating population parameters (mostly means) from these data. The focus will be primarily on generalized linear models (both with and without random effects) for normally and non-normally distributed data. Wherever possible, techniques will be illustrated using real-world examples. Computing will be done using R and SAS.
PUBH 7435 - Latent Variable Measurement Models and Path Analysis
Credits: 3.0 [max 3.0]
Course Equivalencies: 01262 - PubH 7435/PubH 8435
Typically offered: Every Fall
Introduction to use of latent variable models. Exploratory/confirmatory factor analysis, path analysis, structural equation modeling, latent trait models, latent class models. Uses SAS/AMOS software. prereq: [6414, 6415] or [6450, 6451] or biostatistics major or instr consent
PUBH 7440 - Introduction to Bayesian Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Introduction to Bayesian methods. Comparison with traditional frequentist methods. Emphasizes data analysis via modern computing methods: Gibbs sampler, WinBUGS software package. prereq: [[7401 or STAT 5101 or equiv], [public health MPH or biostatistics or statistics] grad student] or instr consent
PUBH 7445 - Statistics for Human Genetics and Molecular Biology
Credits: 3.0 [max 3.0]
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: [6450, [6451 or equiv]] or instr consent; background in molecular biology recommended
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 7470 - Statistics for Translational and Clinical Research
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Diagonostic medicine, including methods for ROC curve. Bioassays. Early-phase clinical trials, methods including dose escalation, toxicity, and monitoring. Qualitiy of life. prereq: [[6450, 6451] or equiv], [grad student in biostatistics or statistics or clinical research], familiarity with SAS
PUBH 7475 - Statistical Learning and Data Mining
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Various statistical techniques for extracting useful information (i.e., learning) from data. Linear discriminant analysis, tree-structured classifiers, feed-forward neural networks, support vector machines, other nonparametric methods, classifier ensembles, unsupervised learning. prereq: [[[6450, 6452] or equiv], programming backgroud in [FORTRAN or C/C++ or JAVA or Splus/R]] or instr consent; 2nd yr MS recommended
PUBH 7415 - Introduction to Clinical Trials
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Summer
Hypotheses/endpoints, choice of intervention/control, ethical considerations, blinding/randomization, data collection/monitoring, sample size, analysis, writing. Protocol development, group discussions. prereq: 6414 or 6450 or one semester graduate-level introductory 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 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 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 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 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 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]