Biostatistics (BST)
BST 3000 - Intro to Statistical Computing
3 Credits
The purpose of this course is to teach students statistical programming and data management skills in SAS and R to aid their careers post-graduation. To this end, they will learn how to use SAS and R to organize and structure data in order to compute epidemiologic measures, descriptive statistics, measures of association and other statistical tests through homework assignments, projects and in-class exercises. They will organize, prepare, interpret, and present statistical test results derived from SAS and R output to the class and through assignments. (Offered in Spring)
Attributes: Public Health Major Elective
BST 3100 - Applied Biostatistics I
3 Credits
This course covers the basic tools of applied statistics for describing categorical and numerical data and making inference to populations, including chi-square and t tests, one-way ANOVA, and simple linear regression procedures. Probability is introduced to the extent it is needed to understand statistical applications. Statistical software is used throughout the course. (Offered Fall and Spring)
Prerequisite(s): (0 Course from MATH 1510-4999, MATH 1400 with a grade of C- or higher, or SLU Math Placement with a minimum score of 1510)
Attributes: Public Health Minor Elective
BST 3200 - Applied Biostatistics II
3 Credits
Linear regression (parameter estimation and tests, confounding, interaction, model building and assessment of fit, diagnostics and remedial measures,); Design of experimental and observational studies (CRD, RCBD, repeated measures, matched pairs, ANOVA, ANOCVA, Contrasts and multiple comparisons, ANCOVA), Logistic Regression (binomial distribution, multinomial distribution, Poisson distribution, and Chi-square test, CMH test, odds, odds ration, relative risk, maximum likelihood, likelihood ratio test, model building and assessment of fit, diagnostics, McNemar's test, conditional likelihood, score test). (Offered in Spring)
BST 3910 - Internship
1-3 Credits (Repeatable up to 6 credits)
Prerequisite(s): (CORE 1000 or UUC Ignite Seminar Waiver with a minimum score of S); CORE 1500*
* Concurrent enrollment allowed.
Attributes: UUC:Reflection-in-Action
BST 3930 - Special Topics
3 Credits (Repeatable for credit)
BST 3980 - Independent Study
1 or 3 Credits (Repeatable for credit)
BST 4100 - Theory of Biostatistics I
3 Credits
Introduction to the principles of probability, statistical distributions, and the extension of these concepts to multiple random variables. Transformations of random variables, moment-generating function technique and central limit theorem. (Offered in Fall)
Prerequisite(s): MATH 2530
BST 4200 - Theory of Biostatistics II
3 Credits
Point estimation, maximum likelihood, regression, sufficient statistics, Bayesian estimation. Interval estimation. Hypothesis testing for one and two samples. Power, likelihood ratio tests. Goodness-of-fit tests, contingency tables. Analysis of variance. (Offered in Spring)
Prerequisite(s): BST 4100
BST 4400 - Introduction to Applied Data Management
3 Credits
This course is a survey of important data management topics and techniques. Topics include: data programming and manipulation, data storage and security, data cleaning, relational database theory and legal and ethical issues of data management. Software tools covered in the course include SPSS, SAS, R, Excel, MySQL, GIT/GitHub, Hadoop, MapReduce, and Python. A number of guest lecturers will present case studies of real-world data management. A key aspect of the course will be weekly labs by in-class working groups. (Offered in Fall)
BST 4930 - Special Topics
3 Credits (Repeatable for credit)
BST 4980 - Advanced Independent Study in Biostatistics
1 or 3 Credits (Repeatable for credit)
BST 5020 - Theory of Biostatistics
3 Credits
This course introduces the principles of probability and biostatistical inferences. Topics covered: role of statistics in scientific research, discrete random variables, continuous random variables, expectation and variance, moments and moment-generating functions, marginal and conditional probability, independence, functions of random variables, sampling distribution, the central limit theorem, methods of statistical estimation, hypothesis testing and confidence interval, and likelihood ratio test. (Offered annually.)
Attributes: Grad Pol Sci Skills, Social Work PhD Methods, Social Work PhD Specilization
BST 5025 - Theory of Biostatistics II
3 Credits
Point estimation, maximum likelihood, regression, sufficient statistics, Bayesian estimation. Interval estimation. Hypothesis testing for one and two samples. Power, likelihood ratio tests. Goodness-of-fit tests, contingency tables. Analysis of variance. (Offered each Spring)
Prerequisite(s): (BST 5020 or BST 4200)
Attributes: MPH-Epidemiology, MPH-Biostatistics, Grad Pol Sci Skills
BST 5030 - Statistical Programming and Study Planning: SAS
3 Credits
This course teaches statistical programming for statistical analysis using SAS software. Programming topics may include: working in the Windows environment, syntax development, creating data sets, reading and manipulating external data files, transforming data, formatting variables, statistical analysis and graphical display.
Attributes: MPH-Behavior Sci & Health Equi, MPH-Global Health, MPH-Health Management & Policy, MPH-Maternal & Child Health, MPH-Public Health Practice
BST 5100 - Introduction to General Linear Modeling
3 Credits
This course presents a unified approach to the application of linear statistical models in biomedical and health services research. Topics include simple and multiple linear regression, Pearson's and non-parametric correlation, analysis of variance (ANOVA), and logistic regression. Both the theory and application of the general linear model (GLM) are presented. (Offered annually.)
Prerequisite(s): (BST 5000, PUBH 5040, or BST 5020*)
* Concurrent enrollment allowed.
Attributes: MPH-Behavior Sci & Health Equi, MPH-Global Health, MPH-Health Management & Policy, MPH-Maternal & Child Health, MPH-Public Health Practice, Social Work PhD Methods
BST 5200 - Survival Data Analysis
3 Credits
This course treats statistical methods for analyzing survival data derived from laboratory, clinical, and epidemiological studies of humans. Both parametric and nonparametric approaches are presented. Focus will be in the practical applications of these methods to clinical and epidemiological research. The SAS and SPSS statistical packages will be used for data management and analysis. (Offered every Fall)
Prerequisite(s): (BST 5000, PUBH 5040, or BST 5020); BST 5030; BST 5100
Attributes: MPH-Global Health, MPH-Maternal & Child Health, MPH-Public Health Practice, Social Work PhD Methods
BST 5210 - Categorical Data Analysis
3 Credits
This course introduces the theory and application of methods for categorical data, with emphasis on biomedical and social science applications. The course will cover the following topics: analysis of two-way, three-way, and higher dimension contingency tables using log-linear model, measures and tests of association for nominal and ordinal tables, logistic regression, weighted least squares, generalized linear models, and the use of computer software analyzing categorical data. (Offered every Fall)
Prerequisite(s): (BST 5000, PUBH 5040, or BST 5020); BST 5030; BST 5100
Attributes: MPH-Global Health, MPH-Maternal & Child Health, Social Work PhD Methods
BST 5220 - Multilevel and Longitudinal Data Analysis
3 Credits
This course will focus on applications of various statistical methods for analyzing longitudinal, or repeated measures, data. The core concepts will cover multilevel analysis, growth curve modeling, and structural equation modeling.
Prerequisite(s): (BST 5000, PUBH 5040, or BST 5020); BST 5030; BST 5100
Attributes: MPH-Epidemiology, MPH-Biostatistics
BST 5230 - Bayesian Statistics
3 Credits
The Bayesian approach to statistics is an alternative to the traditional methods based on hypothesis and significance testing. Bayesian statistics uses prior information and combines it with observable data to quantify knowledge after observing data in what is called the posterior. Simulation is used to approximate this posterior distribution.
Prerequisite(s): (BST 5000, PUBH 5040, or BST 5020); BST 5100
BST 5400 - Applied Data Management
3 Credits
This course is an advanced course on data management for graduate students in the College for Public Health and Social Justice. This course will cover the basic skills necessary for maintaining databases as well as ensuring data quality and manipulating data. The course will also introduce an experiential component in data base design and management. Students will learn advanced concepts and techniques with particular emphasis on applications in public health. Students will learn to use multiple data management and data analysis software packages.
Prerequisite(s): (PUBH 5040 or BST 5020)
Attributes: MPH-Behavior Sci & Health Equi, MPH-Epidemiology, MPH-Global Health, Social Work PhD Specilization
BST 5420 - Sampling Theory and Survey Design in Public Health
3 Credits
This course will provide a survey of the fundamental types of probability sampling designs that are used for data collection with Public Health Surveillance Sample Surveys including: systematic random sampling (including Random-Digit Dialing), simple and stratified random sampling, cluster sampling and multistage sampling. The course will briefly discuss the applications of current survey research methods including web-based surveys and the use cell phones within the context of surveillance systems and registry-based samples. The role of survey design choices in reducing total survey error as well as the role of questionnaire design in reducing non-sampling biases will be discussed briefly. The main topics to be covered in detail include: design-based parameter and variance estimation methods, construction and use of survey weights and statistical models incorporating sample designs as well as the used of survey sampling for registry based samples and matched case/control studies. Methods for evaluating, reducing and adjusting for survey.
Prerequisite(s): (BST 5000, PUBH 5040, or BST 5020)
Attributes: MPH-Epidemiology, MPH-Biostatistics
BST 5450 - Data Visualization
3 Credits
Visual presentation of data in ways that emphasize the structure and significance in data. Guidelines for preparing static graphs, dynamic graphs, interactive graphs, and dashboards. Applies R, including packages R Markdown, ggplot2, plotly, and shiny, and the D3 package in JavaScript.
Attributes: MPH-Biosecurity & Disaster Prp, MPH-Epidemiology, MPH-Global Health, MPH-Health Management & Policy, MPH-Maternal & Child Health, MPH-Public Health Practice, MPH-Biostatistics
BST 5500 - Statistical Learning
3 Credits
Algorithms for learning how to classify variables given a set of predictor variables. Linear regression, logistic regression and linear discriminant analysis. Cross-validation and bootstrapping. Model selection. Ridge regression and the LASSO. Nonlinear models, splines and generalized additive models. Tree-based methods, random forests and boosting. Support vector machines. Unsupervised learning methods are also discussed, including principal components and k-means clustering. (Offered in Spring)
Prerequisite(s): (BST 4100 with a grade of C or higher or (BST 5020 with a grade of C or higher and BST 5100 with a grade of C or higher))
Attributes: MPH-Biostatistics
BST 5600 - R for Spatial Analysis
3 Credits
Geospatial data structures in R. Plotting and exploring data in R. Using R to manage data. Point process analysis using smoothed kernel density estimation and kriging. Variograms and semi-variograms. Spatial autocorrelation in areal data. Moran’s I and Geary’s G. Spatial autogegression. (Offered in Spring)
Prerequisite(s): GIS 5010 with a grade of C or higher; (BST 4100 or BST 5020 with a grade of C or higher)
Attributes: MPH-Biostatistics
BST 5610 - Spatial Epidemiology and Disease Mapping
3 Credits
Statistical methods for disease data that include geographic information. Disease maps and relative risk estimation. Mapping and geographic information systems. Bayesian methods of estimation for conditional autoregressive models. Disease cluster detection. Regression and ecological analysis. (Offered in Fall)
Prerequisite(s): BST 5100 with a grade of C or higher, BST 5600 with a grade of C or higher, and PUBH 5030 with a grade of C or higher
Attributes: MPH-Biostatistics
BST 5620 - Spatio-Temporal Models in Public Health
3 Credits
Models for the spread of epidemics, including the susceptible – infected – removed (SIR) model and some of its generalizations. Kriging in both space and time. Areal data modeling, including the conditional autoregressive model, across time. (Offered in Spring)
Prerequisite(s): BST 5610 with a grade of C or higher; (BST 4200 or BST 5025 with a grade of C or higher)
Attributes: MPH-Biostatistics
BST 5930 - Special Topics
1-3 Credits (Repeatable for credit)
BST 5961 - Master's Project
3 Credits
This course is an independent directed research project. Students work on an applied research project under the guidance of a biostatistics faculty member. Using the skills developed in the Masters of Science in Biostatistics and Health Analytics, each student poses a research question in public health and uses data to address the problem.
BST 5970 - Research Topics in Biostatistics
1-3 Credits (Repeatable for credit)
This course provides direct research experience in biostatistics. Content is developed jointly between the student(s) and a faculty mentor.
BST 5980 - Graduate Independent Study in Biostatistics
1-3 Credits
This course provides specialized study in biostatistics to enhance skills in literature review and problem solving. Content is developed jointly between the student(s) and a faculty mentor.
BST 5985 - Graduate Independent Study in Biostatistics
1-3 Credits (Repeatable for credit)
This course provides an independent study. Content is developed jointly between the student(s) and a faculty mentor.
BST 6100 - Causal Inference
3 Credits
This course is an introduction to causal inference with application sin public health and social justice. Topics include the potential outcomes model, the theory behind and application of randomized trials, and application of causal inference techniques to observational studies. Students will perform analyses with propensity scores, instrumental variables, and graphical causal models (DAGs) and investigate mediation in the causal framework. The focus of the course will be application of methods as opposed to statistical theory.
Prerequisite(s): BST 5030; BST 5100
Attributes: MPH-Epidemiology, MPH-Global Health, MPH-Biostatistics, Social Work PhD Methods
BST 6220 - Multilevel and Longitudinal Data Analysis
3 Credits
This course will focus on applications of various statistical methods for analyzing longitudinal, or repeated measures, data. The core concepts will cover multilevel analysis, growth curve modeling, and structural equation modeling.
Prerequisite(s): (BST 5000, PUBH 5040, or BST 5020); BST 5030; BST 5100
Attributes: MPH-Epidemiology, MPH-Biostatistics
BST 6930 - Special Topics
3 Credits (Repeatable for credit)
BST 6970 - Advanced Research Topics in Biostatistics
1-3 Credits
BST 6980 - Graduate Independent Study in Biostatistics
1-3 Credits (Repeatable for credit)