Package: compound.Cox 3.33

compound.Cox: Univariate Feature Selection and Compound Covariate for Predicting Survival, Including Copula-Based Analyses for Dependent Censoring

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 Biomedicines) <doi:10.3390/biomedicines11030797> and factorial survival analyses (Emura et al 2024 Stat Methods Med Res) <doi:10.1177/09622802231215805>.

Authors:Takeshi Emura [aut, cre], Hsuan-Yu Chen [aut], Shigeyuki Matsui [aut], Yi-Hau Chen [aut]