Program Details : University Catalogs : University of Minnesota

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 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

This course introduces students to a variety of concepts, tools, and techniques that are relevant to the rigorous design and analysis of complex biomedical studies. Topics include ANOVA, sample-size calculations, multiple testing, missing data, prediction, diagnostic testing, smoothing, variable selection, the bootstrap, and nonparametric tests. R software will be used. Biostatistics students are strongly encouraged to typeset their work using LaTeX or in R markdown. prereq: [7405, [STAT 5102 or concurrent registration is required (or allowed) in STAT 5102], biostatistics 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 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. prereq: Regression at the level of PubH 6451 or PubH 7405 or Stat 5302. Familiarity with basic matrix notation and operations (multiplication, inverse, transpose). Working knowledge of SAS or R (PubH 6420).

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]
Typically offered:	Every Spring

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: 7405, [STAT 5101 or STAT 8101]

PUBH 7460 - Advanced Statistical Computing

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Statistical computing using SAS, Splus, and FORTRAN or C. Use of pseudo-random number generators, distribution functions. Matrix manipulations with applications to regression and estimation of variance. Simulation studies, minimization of functions, nonlinear regression, macro programming, numerical methods of integration. prereq: [7405, biostatistics major, [C or FORTRAN]] or instr consent

PUBH 7461 - Exploring and Visualizing Data in R

Credits:	2.0 [max 2.0]
Typically offered:	Every Fall

This course is intended for students, both within and outside the School of Public Health, who want to learn how to manipulate data, perform simple statistical analyses, and prepare basic visualizations using the statistical software R. While the tools and techniques taught will be generic, many of the examples will be drawn from biomedicine and public health.

PUBH 7462 - Advanced Programming and Data Analysis in R

Credits:	2.0 [max 2.0]
Typically offered:	Every Spring

This course is intended for students who are relatively proficient with R, and are looking to improve their coding and data analysis skills. The emphasis will be on learning tools and techniques which are useful to students who will be doing non-trivial programming and/or data analysis in either a research or production environment.

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 7470 - Study Designs in Biomedical Research

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Diagnostic medicine, including methods for ROC curve. Bioassays. Early-phase clinical trials, methods including dose escalation, toxicity, and monitoring. Quality 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 7485 - 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 Ph.D 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. prereq: Background in regression (e.g. linear, logistic, models) at the level of PubH 7405-7406, PubH 6450-6451, PubH 7402, or equiv. Background in statistical theory (Stat 5101-5102 or PubH 7401) is helpful.

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 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 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 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.

MATH 5615H - Honors: Introduction to Analysis I

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall

Axiomatic treatment of real/complex number systems. Introduction to metric spaces: convergence, connectedness, compactness. Convergence of sequences/series of real/complex numbers, Cauchy criterion, root/ratio tests. Continuity in metric spaces. Rigorous treatment of differentiation of single-variable functions, Taylor's Theorem. prereq: [[2243 or 2373], [2263 or 2374], [2283 or 3283]] or 2574

MATH 5616H - Honors: Introduction to Analysis II

Credits:	4.0 [max 4.0]
Typically offered:	Every Spring

Rigorous treatment of Riemann-Stieltjes integration. Sequences/series of functions, uniform convergence, equicontinuous families, Stone-Weierstrass Theorem, power series. Rigorous treatment of differentiation/integration of multivariable functions, Implicit Function Theorem, Stokes' Theorem. Additional topics as time permits. prereq: 5615

STAT 5101 - Theory of Statistics I

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall

Logical development of probability, basic issues in statistics. Probability spaces. Random variables, their distributions and expected values. Law of large numbers, central limit theorem, generating functions, multivariate normal distribution. prereq: (MATH 2263 or MATH 2374 or MATH 2573H), (MATH 2142 or CSCI 2033 or MATH 2373 or MATH 2243)

STAT 5102 - Theory of Statistics II

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall & Spring

Sampling, sufficiency, estimation, test of hypotheses, size/power. Categorical data. Contingency tables. Linear models. Decision theory. prereq: [5101 or Math 5651 or instr consent]

STAT 5401 - Applied Multivariate Methods

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall

Bivariate and multivariate distributions. Multivariate normal distributions. Analysis of multivariate linear models. Repeated measures, growth curve, and profile analysis. Canonical correlation analysis. Principal components and factor analysis. Discrimination, classification, and clustering. pre-req: STAT 3032 or 3301 or 3022 or 4102 or 5021 or 5102 or instr consent Although not a formal prerequisite of this course, students are encouraged to have familiarity with linear algebra prior to enrolling. Please consult with a department advisor with questions.

STAT 5601 - Nonparametric Methods

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall & Spring

Order statistics. Classical rank-based procedures (e.g., Wilcoxon, Kruskal-Wallis). Goodness of fit. Topics may include smoothing, bootstrap, and generalized linear models. prereq: Stat classes 3032 or 3022 or 4102 or 5021 or 5102 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 8777 - Thesis Credits: Master's

Credits:	1.0 -18.0 [max 50.0]
Grading Basis:	No Grade
Typically offered:	Every Fall, Spring & Summer

(No description) prereq: Max 18 cr per semester or summer; 10 cr total required [Plan A only]

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

This course introduces students to a variety of concepts, tools, and techniques that are relevant to the rigorous design and analysis of complex biomedical studies. Topics include ANOVA, sample-size calculations, multiple testing, missing data, prediction, diagnostic testing, smoothing, variable selection, the bootstrap, and nonparametric tests. R software will be used. Biostatistics students are strongly encouraged to typeset their work using LaTeX or in R markdown. prereq: [7405, [STAT 5102 or concurrent registration is required (or allowed) in STAT 5102], biostatistics 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 7450 - Survival Analysis

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

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: 7405, [STAT 5101 or STAT 8101]

STAT 5101 - Theory of Statistics I

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall

Logical development of probability, basic issues in statistics. Probability spaces. Random variables, their distributions and expected values. Law of large numbers, central limit theorem, generating functions, multivariate normal distribution. prereq: (MATH 2263 or MATH 2374 or MATH 2573H), (MATH 2142 or CSCI 2033 or MATH 2373 or MATH 2243)

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 5102 - Theory of Statistics II

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall & Spring

Sampling, sufficiency, estimation, test of hypotheses, size/power. Categorical data. Contingency tables. Linear models. Decision theory. prereq: [5101 or Math 5651 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 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.

GEOG 5561 - Principles of Geographic Information Science

Credits:	4.0 [max 4.0]
Course Equivalencies:	Geog 3561/ Geog 5561
Typically offered:	Every Fall & Spring

Introduction to the study of geographic information systems (GIS) for geography and non-geography students. Topics include GIS application domains, data models and sources, analysis methods and output techniques. Lectures, reading, and hands-on experience with GIS software. prereq: grad

GIS 5571 - ArcGIS I

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

First of a two-course series focusing on ArcGIS Desktop. Overview of ArcGIS system and its use for spatial data processing. Data capture, editing, geometric transformations, map projections, topology, Python scripting, and map production. prereq: [GEOG 5561 or equiv, status in MGIS program, familiarity with computer operating systems] or instr consent

MATH 5615H - Honors: Introduction to Analysis I

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall

Axiomatic treatment of real/complex number systems. Introduction to metric spaces: convergence, connectedness, compactness. Convergence of sequences/series of real/complex numbers, Cauchy criterion, root/ratio tests. Continuity in metric spaces. Rigorous treatment of differentiation of single-variable functions, Taylor's Theorem. prereq: [[2243 or 2373], [2263 or 2374], [2283 or 3283]] or 2574

MATH 5616H - Honors: Introduction to Analysis II

Credits:	4.0 [max 4.0]
Typically offered:	Every Spring

Rigorous treatment of Riemann-Stieltjes integration. Sequences/series of functions, uniform convergence, equicontinuous families, Stone-Weierstrass Theorem, power series. Rigorous treatment of differentiation/integration of multivariable functions, Implicit Function Theorem, Stokes' Theorem. Additional topics as time permits. prereq: 5615

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. prereq: Regression at the level of PubH 6451 or PubH 7405 or Stat 5302. Familiarity with basic matrix notation and operations (multiplication, inverse, transpose). Working knowledge of SAS or R (PubH 6420).

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 7460 - Advanced Statistical Computing

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Statistical computing using SAS, Splus, and FORTRAN or C. Use of pseudo-random number generators, distribution functions. Matrix manipulations with applications to regression and estimation of variance. Simulation studies, minimization of functions, nonlinear regression, macro programming, numerical methods of integration. prereq: [7405, biostatistics major, [C or FORTRAN]] or instr consent

PUBH 7461 - Exploring and Visualizing Data in R

Credits:	2.0 [max 2.0]
Typically offered:	Every Fall

This course is intended for students, both within and outside the School of Public Health, who want to learn how to manipulate data, perform simple statistical analyses, and prepare basic visualizations using the statistical software R. While the tools and techniques taught will be generic, many of the examples will be drawn from biomedicine and public health.

PUBH 7462 - Advanced Programming and Data Analysis in R

Credits:	2.0 [max 2.0]
Typically offered:	Every Spring

This course is intended for students who are relatively proficient with R, and are looking to improve their coding and data analysis skills. The emphasis will be on learning tools and techniques which are useful to students who will be doing non-trivial programming and/or data analysis in either a research or production environment.

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 7470 - Study Designs in Biomedical Research

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Diagnostic medicine, including methods for ROC curve. Bioassays. Early-phase clinical trials, methods including dose escalation, toxicity, and monitoring. Quality 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 7485 - 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 Ph.D 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. prereq: Background in regression (e.g. linear, logistic, models) at the level of PubH 7405-7406, PubH 6450-6451, PubH 7402, or equiv. Background in statistical theory (Stat 5101-5102 or PubH 7401) is helpful.

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

STAT 5401 - Applied Multivariate Methods

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall

Bivariate and multivariate distributions. Multivariate normal distributions. Analysis of multivariate linear models. Repeated measures, growth curve, and profile analysis. Canonical correlation analysis. Principal components and factor analysis. Discrimination, classification, and clustering. pre-req: STAT 3032 or 3301 or 3022 or 4102 or 5021 or 5102 or instr consent Although not a formal prerequisite of this course, students are encouraged to have familiarity with linear algebra prior to enrolling. Please consult with a department advisor with questions.

STAT 5601 - Nonparametric Methods

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall & Spring

Order statistics. Classical rank-based procedures (e.g., Wilcoxon, Kruskal-Wallis). Goodness of fit. Topics may include smoothing, bootstrap, and generalized linear models. prereq: Stat classes 3032 or 3022 or 4102 or 5021 or 5102 or instr consent

WRIT 5051 - Graduate Research Writing for International Students

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall, Spring & Summer

Graduate research writing emphasizes writing techniques, structures, style, and formal language for scholarly writing including research proposals and abstracts, critiques/reviews, and thesis/dissertations and publications. Special focus on field-specific scholarly expectations, documentation, structure/style, grammar, formal or scholarly vocabulary, and extensive revising/editing based on instructor and mentor feedback to meet graduate standards. Discussions. prereq: Grad student

WRIT 5052 - Graduate Research Presentations and Conference Writing for Non-Native Speakers of English

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall & Spring

Practice in writing/presenting graduate-level research for conferences or professional seminars. Delivery of professional academic presentations to U.S. audiences. Conference abstract, paper, and poster presentation. Communication in research process. Students select topics from their own research/studies. Format, style, transitions, topic narrowing, non-verbal presentation skills. prereq: [Grad student, non-native speaker of English] or instr consent

PUBH 7494 - Integrative Learning Experience: Biostatistics

Credits:	1.0 -3.0 [max 3.0]
Grading Basis:	S-N only
Typically offered:	Every Fall, Spring & Summer

MPH students complete an integrative learning experience (ILE) that demonstrates synthesis of foundational and concentration competencies. Students in consultation with faculty select foundational and concentration-specific competencies appropriate to the student?s educational and professional goals. prereq: Biostatistics program, instr consent

University Catalogs

Biostatistics M.S.