compound.Cox: Univariate Feature Selection and Compound Covariate for Predicting Survival

Univariate feature selection and compound covariate methods under the Cox model with high-dimensional features (e.g., gene expressions). Available are survival data for non-small-cell lung cancer patients with gene expressions (Chen et al 2007 New Engl J Med) <doi:10.1056/NEJMoa060096>, statistical methods in Emura et al (2012 PLoS ONE) <doi:10.1371/journal.pone.0047627>, Emura & Chen (2016 Stat Methods Med Res) <doi:10.1177/0962280214533378>, and Emura et al (2019)<doi:10.1016/j.cmpb.2018.10.020>. Algorithms for generating correlated gene expressions are also available. Estimation of survival functions via copula-graphic (CG) estimators is also implemented, which is useful for sensitivity analyses under dependent censoring (Yeh et al 2023) <doi:10.3390/biomedicines11030797>.

Version: 3.30
Depends: numDeriv, survival, MASS
Published: 2023-07-10
DOI: 10.32614/CRAN.package.compound.Cox
Author: Takeshi Emura, Hsuan-Yu Chen, Shigeyuki Matsui, Yi-Hau Chen
Maintainer: Takeshi Emura <takeshiemura at>
License: GPL-2
NeedsCompilation: no
In views: Survival
CRAN checks: compound.Cox results


Reference manual: compound.Cox.pdf


Package source: compound.Cox_3.30.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): compound.Cox_3.30.tgz, r-oldrel (arm64): compound.Cox_3.30.tgz, r-release (x86_64): compound.Cox_3.30.tgz, r-oldrel (x86_64): compound.Cox_3.30.tgz
Old sources: compound.Cox archive

Reverse dependencies:

Reverse depends: Bivariate.Pareto, GFGM.copula, uni.survival.tree


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