This course provides students with hands on experience in clinical research informatics involving secondary use of electronic health record (EHR) data, clinical informatics databases, and basic clinical data science as preparation for more advanced informatics or data science coursework.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Spring.

The central focus of this course is on the generation, modeling, and analysis of data collected in a clinical or biomedical context, with an emphasis on temporal analysis. Analysis techniques will be anchored to solving real-world clinical and biomedical problems.

Grading Basis: Letter Grade

Typically Offered: Spring.

Applied biostatistical methods including descriptive and statistical inference; odds ratio and relative risk, probability theory, parameter estimation, tests for comparing statistics of two or more groups, correlation and linear regression and overviews of: multiple and logistic regression and survival analysis.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Fall, Spring, Summer.

A continuation of BIOS 6601 extending the basic principles of descriptive and inferential statistics to modeling more complex relationships using linear regression, logistic regression, and Cox regression. The statistical package SAS is used extensively. Multiple optional lab sessions offered. Prerequisite: BIOS 6601

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Spring.

This course will emphasize statistical analysis and data interpretation through use of the SAS statistical computing package. Instruction will be provided through laboratory exercises and interactive demonstrations Prereq/Coreq: BIOS 6601 Restriction: Credit may be counted toward a CSPH degree for only one of BIOS 6603, 6604 or 6605

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Summer.

This first course in applied statistics covers basic descriptive methods and probability; parametric and nonparametric inference for the one- and two-sample location problem; ANOVA, ANCOVA, and multiple linear regression. Matrix notation, R, and SAS are used. Prerequesite: differential calculus or permission of instructor

Grading Basis: Letter Grade

A-PUBH BIOS

Typically Offered: Fall.

This is a continuation of BIOS 6611 covering univariate linear modeling and emphasizing multiple regression and analysis of variance. Logistic regression and methods for correlated data are also covered. Matrix algebra and the statistical package SAS will be used. Prereq: BIOS 6611.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Spring.

First of a two-semester applied statistics sequence designed to equip students with a practical knowledge of the quantitative methods most frequently used in medical research. This course is an introduction to applied biostatistics. Concepts will be illustrated using examples in the fields of medicine, biology, epidemiology and public health. Written and graphical presentation and interpretation of methods and results will be emphasized,

Grading Basis: Letter Grade

Typically Offered: Fall.

Second of a two-semester applied statistics sequence designed to equip students with a practical knowledge of the quantitative methods most frequently used in medical research. This course is to develop comfort applying and understanding the mechanisms behind common biostatistical methods. Concepts will be illustrated using examples in the fields of medicine, biology, epidemiology and public health. Written and graphical presentation and interpretation of methods and results will be emphasized.

Grading Basis: Letter Grade

Typically Offered: Spring.

Students will gain experience with statistical consulting and common statistical problems and techniques encountered in consulting through a combination of instruction, real examples, and consultations with investigators. Emphasis will be on methods for effective communication with investigators. Corequesites: BIOS 6611 or consent of instructor.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Fall.

This course teaches the students how to be effective collaborators. Students will learn to modify project hypotheses to be statistical hypotheses. The students will identify and perform the appropriate data analyses and communicate their analyses both verbally and in writing. prerequisite: BIOS 6601 and BIOS 6602 or BIOS 6611 and BIOS 6612 or permission of instructor.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Spring.

This second-year graduate level biostatistics/data science course develops advanced data analysis and collaboration skills. The course is based on five projects using methodologies such as Bayesian analysis, simulation, correlated data, missing data, and study design for grant development. Pre-requisite: BIOS 6611, BIOS 6612, BIOS 6631, BIOS 6632 or permission of the instructor.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Spring.

Covers statistical approaches commonly used in behavioral sciences research, including reliability analysis, exploratory and confirmatory factor analysis, path analysis, structural equation modeling, and advance modeling procedures. Students will analyze data using statistical software, interpret results, and write summaries of findings. Prerequisite: BIOS 6601, BIOS 6602 or equivalent. Cross-listed: CBHS 7010

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Fall.

This course will focus on the application of regression modeling to time-to-event and longitudinal data. Descriptive and inferential methods will be developed for each type of data with an emphasis on graphical inspection at all stages of analysis. Prerequisite BIOS 6601 and 6602 or BIOS 6611 and 6612 and permission of instructor.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Fall.

This course presents an introductory coverage of the theory of discrete and continuous random variables and applications to statistical problems. Topics include probability theory, transformations and expectations, common families of distributions, multiple random variables, and properties of a random sample. Prereq: Differential and integral calculus.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Fall.

This course covers theoretical and applied fundamentals of statistical inference. The course is a continuation of BIOS 6631. The primary topics include point estimation, hypothesis testing, interval estimation and asymptotic methods. Prereq: Differential and integral calculus. Prereq: BIOS 6631

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Spring.

Statistical programming in R, including data managing, vectors, matrices, frames, subscripting, loops, functions, input/output, packages, etc. Concepts and methods for reproducible research will be covered as well as computationally intensive statistical methods. These methods are used to analyze data and present results.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Fall, Spring.

Basic knowledge of and analytic skills in causal inference. Topics include potential outcomes framework for causal inference; experimental and observational studies; identification assumptions for causal parameters; instrumental variable method; regression discontinuity design; propensity score based methods and causal mediation analysis. Prerequisite: BIOS 6611 or BIOS 6602 or permission of instructor; knowledge of R Restriction: Offered in variable terms and years.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Fall, Spring.

This first course in programming using Python covers basic concepts such as variables, data types, iteration, flow of control, input/output, and functions and advanced concepts such as object oriented programming. Statistics related examples, homework and projects may be used.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Spring.

Theory and application of models appropriate for clustered and longitudinal data are studied. Models for different types of outcome variables (e.g., normal , Poisson, binomial) are covered, with an emphasis on linear mixed models for normal outcomes. Prerequisites: BIOS 6632 and BIOS 6612 or permission of instructor.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Fall.

Data Wrangling is the process of getting data into a format which is useful for science. This course will provide students with a diverse set of tools, strategies and practices which can dramatically reduce the pain and wasted time often associated with wrangling and how to leverage the innumerable free resources available to everyone.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Fall.

This course will focus on the development, evaluation and validation of prediction models using observational studies and data, with an emphasis on both model-based and algorithmic approaches. In addition to regular assignments, students will apply their knowledge by developing, evaluating and validating models in three projects.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Spring.

This course will introduce the statistical treatment of time-to-event data with applications to biology, medicine, and public health. It focuses on understanding key methodologies through a strong theoretical foundation, covering nonparametric group comparisons, semi-parametric regression models, parametric models, and state-of-the-art methods for survival analysis. Prerequisites include knowledge of distribution theory, calculus, linear algebra, and programming in R. Prerequisite: BIOS 6611 & BIOS 6631 or instructor permission Corequisite: BIOS 6612 & BIOS 6632 or instructor permission.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Spring.

Design and conduct of clinical research studies. Intended for non-biostatistics students. Topics include: specifying the research question, study endpoints, study populations, study interventions, sample size evaluation, and choice of comparison groups. Common study designs and methods for study conduct are described. Prerequisite: BIOS 6601 or BIOS 6611 or consent of instructor. Offered in odd years.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Fall.

Statistical and scientific design of clinical trials. Intended for biostatistics graduate students. Topics include: scientific and statistical aspects of the research question, endpoints, treatments, sample size evaluation. A wide range of trial designs including group sequential and adaptive trial designs are covered.Pre/Corequisite: BIOS 6612 or instructor permission. Offered spring semester odd years.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Spring.

Masters research paper in Biostatistics is completed under this course.

Grading Basis: Letter Grade with IP

Repeatable. Max Credits: 6.

A-PUBH1 Graduate students and public health certificate students only.

Additional Information: Report as Full Time.

Typically Offered: Fall, Spring, Summer.

This course is designed to give an introduction to statistical methods in genetic association studies. Topics include quantitative and population genetic concepts relevant to genetic association studies, design strategies, and analysis methods for case-control and family data. Pre-Requisite: BIOS 6611, BIOS 6612 (can be co-requisite) or equivalent graduate level (bio)statistics course with instructor consent. Proficiency in coding in statistical software R.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Fall.

This course provides students with hands on experience in solving real life biological problems using the statistical software R and Bioconductor. Students will work and communicate with participating researchers and clinicians on their case studies of genomics data. Pre/Corequisite BIOS 6602 or 6612, or consent of instructor. Offered variable years and terms. Crosslisted with CPBS 7660.

Grading Basis: Letter Grade

Repeatable. Max Credits: 3.

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Spring.

Special interest areas of current biostatistics research and practice are presented and analyzed. The course format is lecture and discussion or seminar. Check with CSPH website for offerings and topics for this course each semester.

Grading Basis: Letter Grade

Repeatable. Max Credits: 99.

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Fall, Spring, Summer.

Students will learn how to use SAS software for data management to prepare data for analyses. Main topics include importing and exporting data, variable and dataset manipulations. Introductions to producing reports, basic statistics, figures and SAS macros are also covered.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Fall.

This course will cover how to use SQL to query data, combine data vertically using set operators and horizontally using joins. Additional topics include incorporating subqueries and how to create and manage tables, views and indexes.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Summer.

This course provides an introduction to Python programming language. Students are introduced to core programming concepts such as variables, types, data structures, conditionals, loops, and functions. This hands-on course includes an overview of the several tools available for writing and running Python.

Grading Basis: Letter Grade

Typically Offered: Fall.

Survey course explores public health informatics topics such as current public health informatics initiatives, data sources, public health information systems, standards, health information exchange, system development/procurement, threats to information security and privacy, and decision support in the public health context.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Spring.

Faculty directed independent study for MPH students in topics related to biostatistics. Restriction: Open only to MPH students. Department Consent Required.

Grading Basis: Letter Grade

Repeatable. Max Credits: 6.

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Fall, Spring, Summer.

Resources of the program are available to those MS students who elect to carry out research in chosen topics related to biostatistics. A faculty member will provide guidance throughout the project. Restriction: Open only to MS students or permission of instructor.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Fall, Spring, Summer.

Biostatistics Master thesis work is completed under this course.

Grading Basis: Letter Grade with IP

A-PUBH1 Graduate students and public health certificate students only.

Additional Information: Report as Full Time.

Typically Offered: Fall, Spring, Summer.

MPH Capstone Preparation will focus on developing the basis for a strong capstone project, culminating in the finalization of the capstone proposal that meets the expectations of the concentration. Prereq: BIOS 6623 or concurrent enrollment in BIOS 6623 or permission of the instructor.

Grading Basis: Satisfactory/Unsatisfactory w/IP

This course is restricted to students with a MPHD-MPH plan of study only.

Typically Offered: Fall, Spring, Summer.

This course will focus on the development, evaluation and validation of prediction models using observational studies and data, with an emphasis on both model-based and algorithmic approaches. Students will develop, evaluate and validate models in 3 projects. Students should be very familiar with programming in R. Prerequisites: BIOS 6611, BIOS 6612, BIOS 6623, BIOS 6631, BIOS 6632

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Spring.

Analysis of genomic data is an integral component of biomedical research. This course will give an introduction to problems in genomics and review both the pioneering and more recent statistical methods developed for analyzing expression data and molecular sequences. BIOS 6611/6612 or BIOS 6631/6632 or permission of instructor. Offered variable term and year.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Fall, Spring.

Advanced special interest areas of current biostatistics research and practice are presented. The course format is lecture and discussion or seminar. Check the CSPH Website for offerings and topics for this course each semester.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Fall, Spring, Summer.

This course will cover statistical models and methods for serially correlated data, including autoregressive models, Markov models, and Markov chain Monte Carlo methods. Prereq: BIOS 6643

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Spring.

This course covers methodological research being carried out for longitudinal studies with missing data. Topics may include missing data mechanisms, non-ignorable missing data, multiple imputation, mixture models and sample size determinations. 1 credit or 2 credit course versions offered in variable years. Prereq: BIOS 6643

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Spring.

This course covers the theory & implementation of estimation algorithms used in statistical analysis. Possible topics: numerical analysis (quadrature), optimization (Newton0Raphson, EM algorithm, stochastic optimization), and simulation (pseudo-random numbers, rejection sampling, Markov chain methods). Prerequisites: BIOS 6611, BIOS 6612, BIOS 6631, BIOS 6632, or permission of instructor. This course is intended for students in the PHD/Biostatistics program.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Fall, Spring.

This course covers theory, application and software for stochastic models commonly used in health sciences, including time to event, recurrent event, multi-type recurrent event, and multi-state models. The intended audience is Biostatistics PhD students. Prerequisite: BIOS 6643 and BIOS 6632 or consent of instructor.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Spring.

This course will introduce students to modern Bayesian statistical modeling and inference. Topics include a comparison of frequentist and Bayesian approaches, Markov Chain Monte Carlo (MCMC) methods for simulating posterior distributions, inference for regression, hierarchical models and mixed models. Prerequisites: BIOS 6612 and BIOS 6632 or permission of instructor. Offered variable term and year. Instructor consent required.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Fall, Spring.

This course will provide students with the computational skills to tackle a biomedical image analysis problem. Students will also improve their Python coding skills, present scientific papers and participate in scientific discussions. Prerequisite: BIOS 6611 or BIOS 6612, linear algebra, experience with Python/Matlab or permission of instructor. Restriction: Offered in variable terms and years.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Fall, Spring.

Information visualization studies interactive visualization techniques for analyzing abstract data. This course introduces design, development, and validation approaches with applications in various biological and biomedical domains. Cross-listed with CPBS 7719.

Grading Basis: Letter Grade

Typically Offered: Spring.

An introduction to key concepts and methods in functional data analysis and their applications in public health. Topics include penalized regression, smoothing and smoothing parameter selection, generalized additive models, sparse functional data, functional regression and functional mixed effects models. BIOS 6612, BIOS 6632 and programming skills in R or permission of instructor. A background in longitudinal data analysis (BIOS 6643) is strongly recommended but not required.

Grading Basis: Letter Grade

Typically Offered: Spring.

An introduction to joint modeling of longitudinal and survival data and its application in health research. Topics include linear mixed effects models, survival analysis, random effects joint model, and possibly dynamic prediction. BIOS 6643 Longitudinal Data or permission of instructor.

Grading Basis: Letter Grade

Typically Offered: Spring.

his course is intended as a survey of methods for choosing an optimal statistical model in the context of biostatistical and public health applications. The course will focus on both the applications of these methods as well as the theory underlying their usage. Prerequisite: BIOS 6611, BIOS 6612, BIOS 6631 and BIOS 6632 or by permission of the Instructor

Grading Basis: Letter Grade

Typically Offered: Spring.

This course will provide the framework for understanding the formal concepts, models and assumptions in statistical theory. Topics include random variables, parameter estimation, measures of performance, hypothesis testing and asymptotic approximations. Prerequisite: BIOS 6632 or equivalent. This course is intended for students in the Biostatistics PhD program.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Fall, Spring.

Interplay of algorithms, their implication and theoretical understanding for certain algorithms and the basics of optimization theory. Implement/prototype algorithms in optimization theory and statistical computing. Learning to read the literature on data science and machine learning and comprehending the algorithmic techniques utilized. Pre-requisite: BIOS 6632 and programming knowledge or equivalent, or permission of Instructor.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Spring.

Theoretical background of unsupervised and supervised machine learning methods and their application to biomedical problem solving. In addition to understanding methodological details, students will learn how to use and apply machine learning methods in Python and improve their coding skills. Prerequisites: Biostatistical methods (e.g. BIOS 6611, BIOS 6612), linear algebra (e.g. MATH 3191) and Python programming (e.g. BIOS 6642), or permission of the instructor.

Grading Basis: Letter Grade

Typically Offered: Fall.

This course will cover the fundamentals and recent advances in deep learning and its applications in biomedical domains. It will include a variety of deep learning algorithms and models, as well as cutting-edge topics such as generative artificial intelligence (AI). Students will learn how to design deep neural networks and apply them to various types of biomedical data. Additionally, students will learn to implement deep models using Python and PyTorch, present deep learning research articles, and write scientific reports. Prereq: BIOS 6618, BIOS 6619, Linear Algebra (e.g. MATH 3191), and Python Programming (e.g. BIOS 6642) or permission of instructor. Coreq: Machine Learning (e.g. BIOS 7747).

Grading Basis: Letter Grade

Typically Offered: Fall.

Scientific and statistical design of clinical trials including the scientific parameterization of outcome space; frequentist and Bayesian standards for scientific evidence and statistical inference; and fixed-sample, group sequential and adaptive trial designs. The course will primarily use R. Prerequisite: BIOS 6624 or permission of instructor.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Fall, Spring.

This course is for the PhD student who wishes to pursue one or more topics in depth. These topics may involved biostatistical material, or biological material necessary to the student's biostatistical work. Supervision by a full-time faculty member is necessary. . Prereq: PhD student or permission of instructor.

Grading Basis: Letter Grade

A-PUBH1 Graduate students and public health certificate students only.

Typically Offered: Fall, Spring, Summer.

PhD Dissertation work is completed under this course.

Grading Basis: Letter Grade with IP

Repeatable. Max Credits: 10.

A-PUBH1 Graduate students and public health certificate students only.

Additional Information: Report as Full Time.

Typically Offered: Fall, Spring, Summer.