coxphMIC: An R Package for Sparse Estimation of Cox Proportional Hazards Models

TL;DR

The paper introduces coxphMIC, an R package that employs a novel sparse estimation method for Cox proportional hazards models, enhancing computational efficiency and inference in survival analysis.

Contribution

It presents a new sparse estimation approach called MIC with reparameterization, implemented in an R package for improved Cox model analysis.

Findings

MIC method is computationally fast.

Reparameterization enforces sparsity and smoothness.

Effective in survival data analysis, demonstrated on PBC data.

Abstract

In this paper, we describe an R package named coxphMIC, which implements the sparse estimation method for Cox proportional hazards models via approximated information criterion (Su et al., 2016 Biometrics). The developed methodology is named MIC which stands for "Minimizing approximated Information Criteria". A reparameterization step is introduced to enforce sparsity while at the same time keeping the objective function smooth. As a result, MIC is computationally fast with a superior performance in sparse estimation. Furthermore, the reparameterization tactic yields an additional advantage in terms of circumventing post-selection inference. The MIC method and its R implementation are introduced and illustrated with the PBC data.