Online Courses

For course information and registration links click on one of the items below

• Regression Modeling Strategies 4-day course
• Introduction to R, RStudio, Regression, and the rms Package 1-day course
  • This is also called the Pre-RMS course
• Combined package of 1-day and 4-day courses with 10% discount

Members of the American Statistical Association get substantial discounts. Full-time members of the following organizations can register for free after sending their official email address used for registration to fh@fharrell.com (BioMarin staff should email drew@dogoodscience.com): Vanderbilt Department of Biostatistics with vanderbilt.edu or vumc.org addresses, Baylor Scott & White Research Institute with bswhealth.org addresses, BioMarin with bmrn.com addresses, and the Nigeria-Vanderbilt Biostatistics Program.

Regression Modeling Strategies

May 14, 15, 18, 19, 2026 9am-4pm CDT

The course offers statistical methodology for rigorous reproducible research, modern methods for developing and validating predictive models, case studies, and use of the R rms package. Emphasis is on developing predictive models, ordinal semiparametric models, model interpretation and validation, and quantifying predictive accuracy, plus many more topics including data reduction (unsupervised learning), navigating the choice of statistical models vs. machine learning, causal thinking in model specification, and an introduction to Bayesian modeling. This course has been well received for more than 20 years and is continually updated. For the first time it is now offered through the ASA/Instats partnership.

Instructors:

Frank Harrell PhD, Professor of Biostatistics, Vanderbilt University School of Medicine
Drew Levy PhD, GoodScience, Inc. 

For details and course registration go to hbiostat.org/course.

Prerequisite: Some knowledge of multiple linear regression. Those not having the prerequisite or who want to learn some R may wish to enroll in the optional 1-day pre-RMS workshop on May 11, 2026 by going to hbiostat.org/course.

The following topics are among those covered in the course:

• broadening statistical modeling skills and increasing model flexibility
• effective sample size
• validation of predictive models
• advantages and disadvantages of statistical models vs. machine learning
• advantages of data reduction (unsupervised learning) over variable selection
• causal thinking in model specification
• multiple imputation while doing strong internal bootstrap validation
• advantages of semiparametric ordinal models for analyzing continuous Y
• ordinal models to generalize the Cox proportional hazards model
• introduction to Bayesian regression models
• directly modeling correlation structure in longitudinal continuous or categorical data
• how R makes it easier to do flexible model building, validation, and interpretation

Target Audience: Statisticians and other quantitative researchers who want to learn some general predictive model development strategies, including data reduction, model specification and variable selection, model validation, relaxing linearity assumptions, and how to choose between machine learning and statistical models.

• Pre-course study
• RMS home page
• Dr. Harrell’s Book - REGRESSION MODELING STRATEGIES with Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis
• Full semester course syllabus
• An introduction to the Harrell”verse” by Nicholas Ollberding
• Old course description
• Old course schedule

Introduction to R, RStudio, Regression, and the rms Package

May 11, 2026 9am-4:30pm CDT

For an introduction to R/RStudio & an efficient comprehensive R workflow taking advantage of 35 years of R/S experience, to learn about the R rms package, or to enhance your multiple regression skills to be ready for the Regression Modeling Strategies 4-day course, take the 1-day Pre-RMS course. For details and course registration go to hbiostat.org/course

The course is offered through the ASA/Instats partnership.

Instructor: Frank Harrell PhD, Professor of Biostatistics, Vanderbilt University School of Medicine
Moderator: Drew Levy PhD, GoodScience, Inc. 

The course covers the following topics:

• scope of R and how to set it up
• introduction to RStudio
• R basics
• introduction to report construction using Quarto
• file import, annotating data, and analysis file creation
• survey of more advanced R Workflow
• simple linear regression
• multiple linear regression
• Regression Modeling Strategies introduction
• R statistical model formulas and fit objects
• introduction to the R rms package to make common task easy, e.g. relaxing linearity assumptions, getting useful statistical tests (including multiple degree of freedom “chunk” tests) and confidence intervals
• linear model case study

Target Audience:

• Statisticians, epidemiologists, data scientists, and others engaged in analysis of quantitative data who want to learn some R programming and report writing skills using RStudio
• Those with some exposure to statistics but who don’t have the general regression skills needed to fully benefit from the 4-day course and who are seeking to learn about simple linear and multiple linear regression models and regression notation
• Those who want to get an introduction to a complete R Workflow for efficiently analyzing data in a reproducible fashion, using Quarto for creating html reports

Even though the 4-day RMS course will not require you to use R interactively, those participants who wish to learn more about R or attain the regression knowledge prerequisite for the 4-day course may wish to take this optional one-day Pre-RMS workshop to enhance R and RStudio) skills, learn about multiple linear regression (a prerequisite for the 4-day course), and to get an introduction to the R rms package that will be used throughout the 4-day course.

Prerequisites:

• General knowledge of algebra and statistics, which can also be acquired by studying Chapters 2-8 of Biostatistics for Biomedical Research before the workshop
• Those not using R previously may want to read Chapter 1 of BBR

Self-Study Courses

• Bayes for Clinical Trials
• Biostatistics for Biomedical Research
• R Workflow