Package: compound.Cox 3.30

Takeshi Emura

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>.

Authors:Takeshi Emura, Hsuan-Yu Chen, Shigeyuki Matsui, Yi-Hau Chen

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compound.Cox/json (API)

# Install 'compound.Cox' in R:
install.packages('compound.Cox', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • Lung - Survival data for patients with non-small-cell lung cancer.
  • PBC - Primary biliary cirrhosis (PBC) of the liver data

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

14 exports 1.00 score 5 dependencies 3 dependents 2 mentions 648 downloads

Last updated 1 years agofrom:0c3c11d00f

Exports:CG.ClaytonCG.FrankCG.GumbelCG.testcindex.CVcompound.regdependCox.regdependCox.reg.CVsurv.factorialuni.scoreuni.selectionuni.WaldX.pathwayX.tag

Dependencies:latticeMASSMatrixnumDerivsurvival