Univariate Feature Selection and Compound Covariate for Predicting Survival
Copula-graphic estimator under the Clayton copula.
Copula-graphic estimator under the Frank copula.
Copula-graphic estimator under the Gumbel copula.
Testing survival difference of two groups via the CG estimators
Cross-validated c-index for measuring the predictive accuracy of a pro...
Univariate Feature Selection and Compound Covariate for Predicting Sur...
Compound shrinkage estimation under the Cox model
Cox regression under dependent censoring.
Univariate Cox regression under dependent censoring.
Factorial survival analysis under dependent censoring
Univariate Cox score test
Univariate feature selection based on univariate significance tests
Univariate Cox Wald test
Generate a matrix of gene expressions in the presence of gene pathways
Generate a matrix of gene expressions in the presence of tag genes
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>.