Predicting survival time for metastatic castration resistant prostate cancer: An iterative imputation approach

Detian Deng, Yu Du, Zhicheng Ji, Karthik Rao, Zhenke Wu, Yuxin Zhu, R. Yates Coley

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we present our winning method for survival time prediction in the 2015 Prostate Cancer DREAM Challenge, a recent crowdsourced competition focused on risk and survival time predictions for patients with metastatic castration-resistant prostate cancer (mCRPC). We are interested in using a patient's covariates to predict his or her time until death after initiating standard therapy. We propose an iterative algorithm to multiply impute right-censored survival times and use ensemble learning methods to characterize the dependence of these imputed survival times on possibly many covariates. We show that by iterating over imputation and ensemble learning steps, we guide imputation with patient covariates and, subsequently, optimize the accuracy of survival time prediction. This method is generally applicable to time-to-event prediction problems in the presence of right-censoring. We demonstrate the proposed method's performance with training and validation results from the DREAM Challenge and compare its accuracy with existing methods.

Original languageEnglish (US)
Article number2672
JournalF1000Research
Volume5
DOIs
StatePublished - 2016

Keywords

  • Ensemble learning
  • Iterative imputation
  • Multiple imputation
  • Survival Time Prediction

ASJC Scopus subject areas

  • General Immunology and Microbiology
  • General Pharmacology, Toxicology and Pharmaceutics
  • General Biochemistry, Genetics and Molecular Biology

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