This repository contains all the code and data used for the paper, which has been published in Statistics in Medicine. In this paper we explore the use of generalized additive models (GAMs) in biomedical research.

The best way to reproduce the paper and its results is to fork this repository and follow the instructions below.

This work was created using R. Therefore, you need to install the latest version of R. Additionally, it is recommended that and IDE such as RStudio is installed as well. Directions to install R and RStudio can be found here.

After installing R and RStudio, you need to install the {bookdown} and {tinytex} (a LaTeX distribution) packages in order to be able to create the PDF output. To install bookdown, you only need to run the following command in the Console in RStudio: install.packages("bookdown").
Instructions to install {tinytex} can be found here. Note that you have to install the package and then run the command install_tinytex() to make it work.

All the required libraries to run the code and create the manuscript can be found in the first code chunk in Main_manuscript.Rmd (which calls all the files in sections to generate the main manuscript). If using RStudio, the first time you open Main_manuscript.Rmd you should automatically receive a warning about the missing packages that need to be installed; you can choose to install all the missing packages then.

Although individual code chunks can be run, it is advised to first generate a document by knitting Main_manuscript.Rmd. If individual chunks of code want to be examined, it is best to open the .Rmd file of interest from sections, and see the code structure to the different functions and code found in scripts (see below).

The different manuscript sections can be found in the sections directory, which contains:

• 01-Background.Rmd: first part of the manuscript.
• 02-Challenges.Rmd which contains code to:
  • Simulate linear and non-linear data
  • Create a plot that visualizes the fits of rm-ANOVA and LMEMs for the simulatd data (linear and non-linear).
• 03-GAM Theory.Rmd , which contains the theory of LMs, GLMs and GLMMs as the foundation to understand GAMs. Also contains code to create a plot that explains how smooths are created in GAMs.
• 04-Longitudinal analysis with GAMs, which contains:
  • Generation of simulated data that follows the trends reported in Figure 3, C in Vishwanath, Karthik, et al. "Using optical spectroscopy to longitudinally monitor physiological changes within solid tumors." Neoplasia 11.9 (2009): 889-900. (Link)
  • Fit a GAM and a rm-ANOVA model to the simulated data.
  • Create incomplete observations in the simulated data.
  • Generate a plot of the raw data and the model fits (full and incomplete data cases)
  • Theory behind empirical Bayesian simultaneous confidence intervals
  • Do pairwise comparisons between smooths for the GAM (full and incomplete data cases)
• 05-Discussion.Rmd: Discussion
• 06-Conclusion.Rmd: Conclusion, acknowledgements.

This work has two appendices, which can be found in the directory appendices:

• Appendix A: Contains the workflow to fit a GAM using simulated data.
• Appendix B: Contains code for functions used through the main manuscript and Appendix A.

Each Appendix .Rmd file (either Appendix_A or Appendix_B) is a stand-alone document that can be compiled independently from each other and the main manuscript. In this way, the reader chooses the section of interest and can examine and run the code independently.

The directory scripts contains the following:

• example.R: Script that simulated linear and quadratic longitudinal data and fits a LMEM and rm-ANOVA.

• plot_example.R: Uses the data from example.R to generate a composite plot showing data and fits.

• basis_functions.R: Simulates longitudinal data with quadratic trend, fits a GAM and extracts the basis functions. Creates combined plot that appears in Figure 2 in the main manuscript. code in R used to generate plots, simulate data, and for obtaining confidence intervals (both across the function and simultaneous) for the fitted smooths.

• simulate_data.R: Simulates normally distributed longitudinal data using the trends found in Vishwanath, Karthik, et al. "Using optical spectroscopy to longitudinally monitor physiological changes within solid tumors." Neoplasia 11.9 (2009): 889-900. (Link)

• pointwise_comparisons.R: When a GAM is fitted to the simulated data from simulate_data.R, this function estimates both the difference between the different smooths and a pointwise (across the function) confidence interval.

• difference_smooths.R: Uses the output from pointwise_comparisons.R to generate a simultaneous confidence interval for the difference. This script is based on the function confint from Gavin Simpson's package gratia, and is licensed under the MIT license.

• pairwise_limits.R: Estimates the regions of significance from the smooth comparisons to add rectangles that highlight the significant time intervals.

• gam_diagnostics.R: Function that uses the source code of gam.check to only generate numerical diagnostics.

The repository also contains directories bibliography for references used in the main manuscript, latex_docs for LaTeX compilation and html_docs if HTML output is desired. Only the LaTeX output is guaranteed to work at this stage.

This entire repository is licensed under a CC BY 4.0 License, which allows reuse with attribution. However, certain files are released under the CC0 1.0 public domain dedication, and others are released under the MIT license. The files indicated below are dual licensed under CC BY 4.0 and CC0 1.0:

• 02-Challenges.Rmd
• 03-GAM Theory.Rmd
• 04-Longitudinal analysis with GAMs
• 07-Appendix.Rmd

The file below is licensed under MIT:

• difference_smooths.R

All other files are under CC BY 4.0.

If you have questions or comments please contact Ariel Mundo (ariel.mundo.ortiz.chum@ssss.gouv.qc.ca)