Pre-Regression Modeling Strategies Workshop

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

 

The course takes place on Monday May 12, 2025, 9am-4:30pm US Central Daylight Time. The May 2025 course video will be available in early June at which time this page will be updated.

Registration

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.

The Pre-RMS course is given by Frank Harrell and moderated by Drew Levy.

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

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

Detailed Course Topics

• Scope of R and how to set it up
• Introduction to RStudio
• R basics
• Learning R from scripts
• Brief introduction to report construction using Quarto
• File import, annotating data, and analysis file creation
• Survey of R Workflow
• Simple linear regression
• Multiple linear regression
• Regression Modeling Strategies Introduction
  • Section 1.2: examples of use of predictive multivariable modeling
  • Section 1.4: planning for modeling
  • Section 2.1: notation
  • Section 2.2: model formulations
  • Section 2.3: interpreting model parameters
    • 2.3.1: nominal predictors
    • 2.3.2: interactions
    • 2.3.3: inference for a simple model
    • 2.3.4: composite (chunk) tests
• Regression in R: the statistical formula language and fit objects
• R rms package: fitting, diagnosing, plotting to understand the model, general point estimates, getting predictions, chunk tests (ANOVA)
• Linear model case study: Estimating body fat

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This course is not under the auspices of Vanderbilt University.