useR! 2024

This introductory tutorial is designed to equip participants with practical skills and knowledge for performing survival analysis using machine learning techniques. Survival analysis, a fundamental statistical method in biomedical and clinical research, focuses on analyzing time-to-event data, such as the time to disease progression or patient survival. In this tutorial, attendees will work with clinical and gene expression data to build, train, and test survival models. They will learn how to leverage R's mlr3 ecosystem for efficient model development, incorporating sophisticated machine learning models such as penalized linear models and random forests to enhance the accuracy of the survival predictions. Participants will also explore survival metrics and model validation techniques to assess the quality and reliability of their models in the context of real-world data. Whether you're new to survival analysis or seeking to enhance your skills, this workshop offers valuable insights and hands-on experience for tackling challenging clinical and biomedical questions.

Requirements for participating:

• Bring a laptop with the latest version of R installed: https://www.r-project.org/ 
• Install the following libraries after 1 July (to ensure you have the latest version):
  • tidyverse (CRAN)
  • mlr3verse (CRAN)
  • survival (CRAN)
  • rpart (CRAN)
  • mlr3proba (GitHub)
  • mlr3extralearners (GitHub)
• Install some models that we will try:
  • glmnet (CRAN) [Required]
  • ranger (CRAN) [Optional]
  • CoxBoost (GitHub) [Optional] 
  • aorsf (GitHub) [Optional]

References:

• [1] Bischl, B., Sonabend, R., Kotthoff, L., & Lang, M. (Eds.). (2024). "Applied Machine Learning Using mlr3 in R". CRC Press. https://mlr3book.mlr-org.com
• [2] Sonabend, R., Király, F. J., Bender, A., Bischl, B., & Lang, M. (2021). mlr3proba: an R package for machine learning in survival analysis. Bioinformatics, 37(17), 2789–2791. https://doi.org/10.1093/BIOINFORMATICS/BTAB039
• [3] Zhao, Z., Zobolas, J., Zucknick, M., & Aittokallio, T. (2024). Tutorial on survival modeling with applications to omics data. Bioinformatics. https://doi.org/10.1093/BIOINFORMATICS/BTAE132 [Tutorial link]

Registration:
To add this tutorial to your registration, log in to your existing registration, click the Modify Registration button, and navigate to the Reg Options page (page 4). Select the tutorial you want to attend.

Enhancing scientific publishing through Quarto is the workshop's focus, designed for those familiar with R Markdown and newcomers to Quarto. Quarto extends the foundational capabilities of R Markdown, introducing advanced features that elevate publishing projects. The tutorial navigates through Quarto's core functionalities, From converting R Markdown files to creating new Quarto content from scratch while exploiting Quarto's full spectrum for sophisticated, publication-ready outputs. Attendees will explore the broad range of Quarto's applications, including single-document formats, as well as more complex Quarto projects. The workshop provides hands-on experiences designed for interactive participation, enabling attendees to integrate Quarto's tools into known publishing workflows and learning to initiate new projects. At the end of the session, participants will have the skills needed to upgrade their current R Markdown workflows and start new projects with Quarto. This will enable them to create refined scientific publications that communicate research findings and results effectively.

Registration:
To add this tutorial to your registration, log in to your existing registration, click the Modify Registration button, and navigate to the Reg Options page (page 4). Select the tutorial you want to attend.