Clinical Reporting with {gtsummary}

R/Medicine 2022 Workshop

πŸ—“οΈ August 23, 2022 | 11:00am - 2:00pm EDT

🏨 Virtual

πŸ’₯ FREE with conference registration ($10 to $50)

πŸ“ To register for the workshop, follow instructions in the email β€œR/Medicine 2022 - How to Prepare” you received after conference registration

Overview

The gtsummary package provides an elegant and flexible way to create publication-ready summary tables in R. A critical part of the work of statisticians, data scientists, and analysts is summarizing data sets and regression models in R and publishing or sharing polished summary tables. The gtsummary package was created to streamline these everyday analysis tasks by allowing users to easily create reproducible summaries of data sets, regression models, survey data, and survival data with a simple interface and very little code. The package follows a tidy framework, making it easy to integrate with standard data workflows, and offers many table customization features through function arguments, helper functions, and custom themes.

Learning objectives

Build, customize, and report tables often found in medical journals and other research-related publications.

Is this course for me?

If your answer to any of the following questions is β€œyes”, then this is the right workshop for you.

  • Do you make summary tables in R (data, survey data, regression models, time-to-event data, adverse event reports)?

  • Do you want your workflow to be reproducible?

  • Are you often frustrated with the immense amount of code required to create great-looking tables in R?

The workshop is designed for those with some experience in R. It will be expected that you can perform basic data manipulation. Experience with the {tidyverse} and the %>% operator is a plus, but not required.

Prework

Before attending the workshop please have the following installed and configured on your machine.

  • Recent version of R

  • Recent version of RStudio

  • Most recent release of the gtsummary and other packages used in workshop.

    instll_pkgs <- 
  c("gtsummary", "tidyverse", "labelled", "usethis", 
    "causaldata", "fs", "skimr", "car", "emmeans")
install.packages(instll_pkgs)

  • Ensure you can knit R markdown documents

    • Open RStudio and create a new Rmarkdown document
    • Save the document and check you are able to knit it.

Instructor

Headshot of Daniel D. Sjoberg

Daniel D. Sjoberg (he/him) is a Senior Biostatistician at Memorial Sloan Kettering Cancer Center in New York City and a DrPH candidate in Biostatistics at Columbia University. His research interests include adaptive methods in clinical trials, precision medicine, and predictive modeling. He also enjoys R package development, creating many packages available on CRAN, R-Universe, GitHub, and internally at MSKCC. Daniel is the winner of the 2021 American Statistical Association (ASA) Innovation in Statistical Programming and Analytics award.