Supervised Dimension Reduction for Large-Scale 'Omics' Data with Censored Survival Outcomes under Possible Non-Proportional Hazards

Lauren Spirko-Burns, Karthik Devarajan

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


The past two decades have witnessed significant advances in high-throughput 'omics' technologies such as genomics, proteomics, metabolomics, transcriptomics and radiomics. These technologies have enabled simultaneous measurement of the expression levels of tens of thousands of features from individual patient samples and have generated enormous amounts of data that require analysis and interpretation. One specific area of interest has been in studying the relationship between these features and patient outcomes, such as overall and recurrence-free survival, with the goal of developing a predictive 'omics' profile. Large-scale studies often suffer from the presence of a large fraction of censored observations and potential time-varying effects of features, and methods for handling them have been lacking. In this paper, we propose supervised methods for feature selection and survival prediction that simultaneously deal with both issues. Our approach utilizes continuum power regression (CPR) - a framework that includes a variety of regression methods - in conjunction with the parametric or semi-parametric accelerated failure time (AFT) model. Both CPR and AFT fall within the linear models framework and, unlike black-box models, the proposed prognostic index has a simple yet useful interpretation. We demonstrate the utility of our methods using simulated and publicly available cancer genomics data.

Original languageEnglish
Pages (from-to)2032-2044
Number of pages13
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number5
StatePublished - 2021


  • Buckley-James method
  • Continuum power regression
  • accelerated failure time model
  • censored survival data
  • generalized F
  • high-throughput omics
  • non-proportional hazards


Dive into the research topics of 'Supervised Dimension Reduction for Large-Scale 'Omics' Data with Censored Survival Outcomes under Possible Non-Proportional Hazards'. Together they form a unique fingerprint.

Cite this