Survival Prediction Based on Compound Covariate under Cox Proportional Hazard Models

Takeshi Emura, Yi Hau Chen*, Hsuan Yu Chen

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

24 Scopus citations


Survival prediction from a large number of covariates is a current focus of statistical and medical research. In this paper, we study a methodology known as the compound covariate prediction performed under univariate Cox proportional hazard models. We demonstrate via simulations and real data analysis that the compound covariate method generally competes well with ridge regression and Lasso methods, both already well-studied methods for predicting survival outcomes with a large number of covariates. Furthermore, we develop a refinement of the compound covariate method by incorporating likelihood information from multivariate Cox models. The new proposal is an adaptive method that borrows information contained in both the univariate and multivariate Cox regression estimators. We show that the new proposal has a theoretical justification from a statistical large sample theory and is naturally interpreted as a shrinkage-type estimator, a popular class of estimators in statistical literature. Two datasets, the primary biliary cirrhosis of the liver data and the non-small-cell lung cancer data, are used for illustration. The proposed method is implemented in R package "compound.Cox" available in CRAN at

Original languageEnglish
Article numbere47627
JournalPLoS ONE
Issue number10
StatePublished - 24 10 2012
Externally publishedYes


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