Multi-task Survival Analysis

Collecting labeling information of time-to-event analysis is naturally very time consuming, i.e., one has to wait for the occurrence of the event of interest, which may not always be observed for every instance. By taking advantage of censored instances, survival analysis methods internally consider more samples than standard regression methods, which partially alleviates this data insufficiency problem. Whereas most existing survival analysis models merely focus on a single survival prediction task, when there are multiple related survival prediction tasks, we may benefit from the tasks relatedness. Simultaneously learning multiple related tasks, multi-task learning (MTL) provides a paradigm to alleviate data insufficiency by bridging data from all tasks and improves generalization performance of all tasks involved. Even though MTL has been extensively studied, there is no existing work investigating MTL for survival analysis. In this paper, we propose a novel multi-task survival analysis framework that takes advantage of both censored instances and task relatedness. Specifically, based on two common used task relatedness assumptions, i.e., low-rank assumption and cluster structure assumption, we formulate two concrete models, COX-TRACE and COX-cCMTL, under the proposed framework, respectively. We develop efficient algorithms and demonstrate the performance of the proposed multi-task survival analysis models on the The Cancer Genome Atlas (TCGA) dataset. Our results show that the proposed approaches can significantly improve the prediction performance in survival analysis and can also discover some inherent relationships among different cancer types.

[1]  Elisa Lee,et al.  Statistical Methods for Survival Data Analysis: Lee/Survival Data Analysis , 2003 .

[2]  P. Grambsch,et al.  A Package for Survival Analysis in S , 1994 .

[3]  E. Kaplan,et al.  Nonparametric Estimation from Incomplete Observations , 1958 .

[4]  Jieping Ye,et al.  Transfer Learning for Survival Analysis via Efficient L2,1-Norm Regularized Cox Regression , 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

[5]  F. Harrell,et al.  Evaluating the yield of medical tests. , 1982, JAMA.

[6]  H. Zou,et al.  A cocktail algorithm for solving the elastic net penalized Cox’s regression in high dimensions , 2013 .

[7]  Jiayu Zhou,et al.  A multi-task learning formulation for predicting disease progression , 2011, KDD.

[8]  W. Travis,et al.  Lung cancer , 1995, Cancer.

[9]  Jieping Ye,et al.  A Multi-Task Learning Formulation for Survival Analysis , 2016, KDD.

[10]  Jieping Ye,et al.  An accelerated gradient method for trace norm minimization , 2009, ICML '09.

[11]  B. Efron.  The Efficiency of Cox's Likelihood Function for Censored Data , 1977 .

[12]  Jian Huang,et al.  BMC Bioinformatics BioMed Central Methodology article Supervised group Lasso with applications to microarray data , 2007 .

[13]  Massimiliano Pontil,et al.  Convex multi-task feature learning , 2008, Machine Learning.

[14]  R. Tibshirani.  The lasso method for variable selection in the Cox model. , 1997, Statistics in medicine.

[15]  Sheung Tat Fan,et al.  Twist Overexpression Correlates with Hepatocellular Carcinoma Metastasis through Induction of Epithelial-Mesenchymal Transition , 2006, Clinical Cancer Research.

[16]  Russell Greiner,et al.  Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors , 2011, NIPS.

[17]  Eric P. Xing,et al.  Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity , 2009, ICML.

[18]  Massimiliano Pontil,et al.  Regularized multi--task learning , 2004, KDD.

[19]  Fei Wang,et al.  From micro to macro: data driven phenotyping by densification of longitudinal electronic medical records , 2014, KDD.

[20]  Fabrizio Silvestri,et al.  Improving Post-Click User Engagement on Native Ads via Survival Analysis , 2016, WWW.

[21]  Yurii Nesterov,et al.  Introductory Lectures on Convex Optimization - A Basic Course , 2014, Applied Optimization.

[22]  Jieping Ye,et al.  Multi-Task Feature Learning Via Efficient l2, 1-Norm Minimization , 2009, UAI.

[23]  Chris H. Q. Ding,et al.  Spectral Relaxation for K-means Clustering , 2001, NIPS.

[24]  Thomas Lengauer,et al.  Multi-task learning for HIV therapy screening , 2008, ICML '08.

[25]  Mohammad Modarres,et al.  Reliability engineering and risk analysis : a practical guide , 2016 .

[26]  Jiayu Zhou,et al.  Clustered Multi-Task Learning Via Alternating Structure Optimization , 2011, NIPS.

[27]  Rich Caruana,et al.  Multitask Learning , 1997, Machine-mediated learning.

[28]  D.,et al.  Regression Models and Life-Tables , 2022 .

[29]  Hongzhe Li,et al.  In Response to Comment on "Network-constrained regularization and variable selection for analysis of genomic data" , 2008, Bioinform..

[30]  Trevor Hastie,et al.  Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent. , 2011, Journal of statistical software.

[31]  Jean-Philippe Vert,et al.  Clustered Multi-Task Learning: A Convex Formulation , 2008, NIPS.

[32]  Lee-Jen Wei,et al.  The accelerated failure time model: a useful alternative to the Cox regression model in survival analysis. , 1992, Statistics in medicine.

[33]  Moshe Talpaz,et al.  Dasatinib (BMS-354825) Tyrosine Kinase Inhibitor Suppresses Invasion and Induces Cell Cycle Arrest and Apoptosis of Head and Neck Squamous Cell Carcinoma and Non–Small Cell Lung Cancer Cells , 2005, Clinical Cancer Research.

[34]  Genevera I. Allen,et al.  TCGA2STAT: simple TCGA data access for integrated statistical analysis in R , 2016, Bioinform..