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). 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 (Leeb and Pötscher, 2005). The MIC method and its R implementation are introduced and illustrated with the PBC data.
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For attribution, please cite this work as
Nabi & Su, "coxphMIC: An R Package for Sparse Estimation of Cox Proportional Hazards Models via Approximated Information Criteria", The R Journal, 2017
BibTeX citation
@article{RJ-2017-018,
author = {Nabi, Razieh and Su, Xiaogang},
title = {coxphMIC: An R Package for Sparse Estimation of Cox Proportional Hazards Models via Approximated Information Criteria},
journal = {The R Journal},
year = {2017},
note = {https://doi.org/10.32614/RJ-2017-018},
doi = {10.32614/RJ-2017-018},
volume = {9},
issue = {1},
issn = {2073-4859},
pages = {229-238}
}