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FSGLmstate Package Logo

FSGLmstate is an R package that performs variable selection via fused sparse-group lasso (FSGL) penalized multi-state models (Miah et al., 2024).

Abstract

In multi-state models based on high-dimensional data, effective modeling strategies are required to determine an optimal, ideally parsimonious model. In particular, linking covariate effects across transitions is needed to conduct joint variable selection. A useful technique to reduce model complexity is to address homogeneous covariate effects for distinct transitions. We integrate this approach to data-driven variable selection by extended regularization methods within multi-state model building. We propose the fused sparse-group lasso (FSGL) penalized Cox-type regression in the framework of multi-state models combining the penalization concepts of pairwise differences of covariate effects along with transition-wise grouping. For optimization, we adapt the alternating direction method of multipliers (ADMM) algorithm to Cox-type hazards regression in the multi-state setting. In a simulation study and application to acute myeloid leukemia (AML) data, we evaluate the algorithm's ability to select a sparse model incorporating relevant transition-specific effects and similar cross-transition effects. We investigate settings in which the combined penalty is beneficial compared to global lasso regularization.

Table of Contents

Installation

You can install the development package version FSGLmstate from GitHub with:

# install.packages("devtools")
devtools::install_github("k-miah/FSGLmstate")

Usage

Load the package in R with:

library(FSGLmstate)

Main Features

  • penalty_matrix_K(): Generation of a penalty structure matrix for use in penalized regression incorporating lasso, fused and group-lasso penalties
  • fit.admm.fsgl.mstate(): Alternating direction method of multipliers (ADMM) optimization for FSGL penalized multi-state models for fixed set of tuning parameters
  • gcv.fit.admm.fsgl.mstate(): Alternating direction method of multipliers (ADMM) optimization for FSGL penalized multi-state models for optimal tuning parameters via generalized cross-validation (GCV) selection criterion

Bugs and issues can be reported at https://github.com/k-miah/FSGLmstate/issues.

Contact

For any questions or feedback, please reach out to k.miah@dkfz.de.