Cox MultiBlock Survival

This software package provides Cox survival analysis for high-dimensional and multiblock datasets. It encompasses a suite of functions dedicated from the classical Cox regression to newest analysis, including Cox proportional hazards model, Stepwise Cox regression, and Elastic-Net Cox regression, Sparse Partial Least Squares Cox regression (sPLS-COX) incorporating three distinct strategies, and two Multiblock-PLS Cox regression (MB-sPLS-COX) methods. This tool is designed to adeptly handle high-dimensional data, and provides tools for cross-validation, plot generation, and additional resources for interpreting results. While references are available within the corresponding functions, key literature is mentioned below. Terry M Therneau (2024) < https://CRAN.R-project.org/package=survival>, Noah Simon et al. (2011) , Philippe Bastien et al. (2005) , Philippe Bastien (2008) , Philippe Bastien et al. (2014) , Kassu Mehari Beyene and Anouar El Ghouch (2020) , Florian Rohart et al. (2017) .

• Introduction

• Installation

• Getting started

• Contact

• References

Introduction

The Coxmos R package is an end-to-end pipeline designed for the study of survival analysis for high dimensional data. Updating classical methods and adding new ones based on sPLS technologies. Furthermore, includes multiblock functions to work with multiple sets of information to improve survival accuracy.

The pipeline includes three basic analysis blocks:

1. Computing cross-validation functions and getting the models.

2. Evaluating all the models to select the better one for multiple metrics.

3. Understanding the results in terms of the global model and the original variables.

Coxmos contains the necessary functions and documentation to obtain from raw data the final models after compare them, evaluate with test data, study the performance individually and in terms of components and graph all the results to understand which variables are more relevant for each case of study.

Installation

Dependencies requiring manual installation

Some of the metrics available in Coxmos are optional based and will not be included in the standard Coxmos installation. A list of all optional packages are shown below:

• nsROC:
• smoothROCtime:
• survivalROC:
• risksetROC:
• ggforce:
• RColorConesa:

Installing Coxmos

The Coxmos R package and all the remaining dependencies can be installed from CRAN:

install.packages("Coxmos")

Or from GitHub using devtools

devtools::install_github("BiostatOmics/Coxmos")

In case of using Github, to access vignettes, you will need to force building with devtools::install_github(build_vignettes = TRUE). Please note that this will also install all suggested packages required for vignette build and might increase install time. Alternatively, an HTML version of the vignette is available under the vignettes folder.

Getting started

In order to use Coxmos, you will need the following items:

• An explanatory X matrix.
• A response survival Y matrix (with two columns, "time" and "event").

Please note that two toy datasets are included in the package. Details to load and use them can be found in the package's vignette.

Contact

If you encounter a problem, please open an issue via GitHub.

References

If you use Coxmos in your research, please cite the original publication: