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Salerno S, Li Y. High-Dimensional Survival Analysis: Methods and Applications. ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION 2023; 10:25-49. [PMID: 36968638 PMCID: PMC10038209 DOI: 10.1146/annurev-statistics-032921-022127] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
In the era of precision medicine, time-to-event outcomes such as time to death or progression are routinely collected, along with high-throughput covariates. These high-dimensional data defy classical survival regression models, which are either infeasible to fit or likely to incur low predictability due to over-fitting. To overcome this, recent emphasis has been placed on developing novel approaches for feature selection and survival prognostication. We will review various cutting-edge methods that handle survival outcome data with high-dimensional predictors, highlighting recent innovations in machine learning approaches for survival prediction. We will cover the statistical intuitions and principles behind these methods and conclude with extensions to more complex settings, where competing events are observed. We exemplify these methods with applications to the Boston Lung Cancer Survival Cohort study, one of the largest cancer epidemiology cohorts investigating the complex mechanisms of lung cancer.
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Affiliation(s)
- Stephen Salerno
- Department of Biostatistics, University of Michigan, Ann Arbor, United States, 48109
| | - Yi Li
- Department of Biostatistics, University of Michigan, Ann Arbor, United States, 48109
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Yue C, Xuejun M, Yaguang L, Lei H. A penalized estimation for the Cox model with ordinal multinomial covariates. J STAT COMPUT SIM 2021. [DOI: 10.1080/00949655.2021.1989692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Chao Yue
- Department of Statistics, School of Mathematical Sciences, Soochow University, Suzhou, China
| | - Ma Xuejun
- Department of Statistics, School of Mathematical Sciences, Soochow University, Suzhou, China
| | - Li Yaguang
- Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei, China
| | - Huang Lei
- Department of Statistics, School of Mathematics, Southwest Jiaotong University, Chengdu, China
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Liu L, Qu J, Dai Y, Qi T, Teng X, Li G, Qu Q. An interactive nomogram based on clinical and molecular signatures to predict prognosis in multiple myeloma patients. Aging (Albany NY) 2021; 13:18442-18463. [PMID: 34260414 PMCID: PMC8351694 DOI: 10.18632/aging.203294] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 06/23/2021] [Indexed: 12/29/2022]
Abstract
Although novel drugs and treatments have been developed and improved, multiple myeloma (MM) is still recurrent and difficult to cure. In the present study, the magenta module containing 400 hub genes was determined from the training dataset of GSE24080 through weighted gene co-expression network analysis (WGCNA). Then, using the least absolute shrinkage and selection operator (Lasso) analysis, a fifteen-gene signature was firstly selected and the predictive performance for overall survival (OS) was favorable, which was identified by Receiver Operating Characteristic (ROC) curves. The risk score model was constructed based on survival-associated fifteen genes from the Lasso model, which classified MM patients into high-risk and low-risk groups. Areas under the curve (AUC) of ROC curve and log-rank test showed that the high-risk group was correlated to the dismal survival outcome of MM patients, which was also identified in testing dataset of GSE9782. The calibration plot, the AUC value of the ROC curve and Concordance-index showed that the interactive nomogram with risk score could favorably predict the probability of multi-year OS of MM patients. Therefore, it may help clinicians make a precise therapeutic decision based on the easy-to-use tool of the nomogram.
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Affiliation(s)
- Linxin Liu
- Department of Hematology, Xiangya Hospital, Central South University, Changsha, China
| | - Jian Qu
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Institute of Clinical Pharmacy, Central South University, Changsha, China
| | - Yuxin Dai
- Department of Biochemistry and Molecular Biology, School of Life Sciences, Central South University, Changsha, China
| | - Tingting Qi
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Institute of Clinical Pharmacy, Central South University, Changsha, China
| | - Xinqi Teng
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Institute of Clinical Pharmacy, Central South University, Changsha, China
| | - Guohua Li
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Institute of Clinical Pharmacy, Central South University, Changsha, China
| | - Qiang Qu
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, China.,Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
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van de Wiel MA, van Nee MM, Rauschenberger A. Fast Cross-validation for Multi-penalty High-dimensional Ridge Regression. J Comput Graph Stat 2021. [DOI: 10.1080/10618600.2021.1904962] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Mark A. van de Wiel
- Department of Epidemiology and Data Science, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Mirrelijn M. van Nee
- Department of Epidemiology and Data Science, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Armin Rauschenberger
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
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Abstract
As datasets continue to increase in size, there is growing interest in methods for prediction that are both Received January 2018 flexible and interpretable. A flurry of recent work on this topic has focused on additive modeling in the Revised February 2019 regression setting, and in particular, on the use of data-adaptive nonlinear functions that can be used to flexibly model each covariate's effect, conditional on the other features in the model. In this article, we extend this recent line of work to the survival setting. We develop an additive Cox proportional hazards model, in which each additive function is obtained by trend filtering, so that the fitted functions are piece-wise polynomial with adaptively chosen knots. An efficient proximal gradient descent algorithm is used to fit the model. We demonstrate its performance in simulations and in application to a primary biliary cirrhosis data set, as well as a dataset consisting of time to publication for clinical trials in the biomedical literature. Supplementary materials for this article are available online.
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Affiliation(s)
- Jiacheng Wu
- Department of Biostatistics, University of Washington, Seattle, WA
| | - Daniela Witten
- Department of Biostatistics, University of Washington, Seattle, WA.,Department of Statistics, University of Washington, Seattle, WA
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