Compound.Cox: Univariate feature selection and compound covariate for predicting survival. (January 2019)

• Record Type: Journal Article
• Authors: Emura, Takeshi; Matsui, Shigeyuki; Chen, Hsuan-Yu
• Is Part Of: Computer methods and programs in biomedicine. Volume 168(2019)
• Page Start: 21
• Page End: 37
• Publication Date: 2019-01
• Subjects: Cancer prognosis -- Copula -- Cox regression -- Cross-validation -- Dependent censoring -- False discovery rate -- Gene expression -- High-dimensional data -- Multiple testing; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.10.020
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Feature selection algorithms using multiple tests. Screening for high-dimensional features or gene expressions. A cross-validation algorithm to measure predictive capability of selected features. Computation of the false discovery rate. Multigene predictors for survival (e.g., the compound covariate predictor). Survival data on lung cancer patients made available. Copula-based methods for survival data with dependent censoring. Abstract: Background and objective: Univariate feature selection is one of the simplest and most commonly used techniques to develop a multigene predictor for survival. Presently, there is no software tailored to perform univariate feature selection and predictor construction. Methods: We develop the compound.Cox R package that implements univariate significance tests (via the Wald tests or score tests) for feature selection. We provide a cross-validation algorithm to measure predictive capability of selected genes and a permutation algorithm to assess the false discovery rate. We also provide three algorithms for constructing a multigene predictor (compound covariate, compound shrinkage, and copula-based methods), which are tailored to the subset of genes obtained from univariate feature selection. We demonstrate our package using survival data on the lung cancer patients. We examine the predictive capability of the developed algorithms by the lung cancer data and simulated data. Results: The developed R package, compound.Cox, is available on the CRAN repository. The statistical tools in compound.Cox allow researchers to determine an optimal significance level of the tests, thus providing researchers an optimal subset of genes for prediction. The package also allows researchers to compute the false discovery rate and various prediction algorithms. Graphical abstract: