title: Predicting survival time for metastatic castration resistant prostate cancer: An iterative imputation approach.
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.
authors.affiliation: Department of Biostatistics, Johns Hopkins University, Baltimore, USA.
authors.forename: Detian
authors.initials: D
authors.lastname: Deng
authors.affiliation: Department of Biostatistics, Johns Hopkins University, Baltimore, USA.
authors.forename: Yu
authors.initials: Y
authors.lastname: Du
authors.affiliation: Department of Biostatistics, Johns Hopkins University, Baltimore, USA.
authors.forename: Zhicheng
authors.initials: Z
authors.lastname: Ji
authors.affiliation: School of Medicine, Johns Hopkins University, Baltimore, USA.
authors.forename: Karthik
authors.initials: K
authors.lastname: Rao
authors.affiliation: Department of Biostatistics, Johns Hopkins University, Baltimore, USA.
authors.forename: Zhenke
authors.initials: Z
authors.lastname: Wu
authors.affiliation: Department of Biostatistics, Johns Hopkins University, Baltimore, USA.
authors.forename: Yuxin
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: Department of Biostatistics, Johns Hopkins University, Baltimore, USA.
authors.forename: R Yates
authors.initials: RY
authors.lastname: Coley
keywords: Ensemble learning; Iterative imputation; Survival Time Prediction; multiple imputation