Review of statistical methods for survival analysis using genomic data
Survival analysis mainly deals with the time to event, including death, onset of disease, and bankruptcy. The common characteristic of survival analysis is that it contains “censored” data, in which the time to event cannot be completely observed, but instead represents the lower bound of the time t...
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| Language: | English |
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BioMed Central
2019-12-01
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| Series: | Genomics & Informatics |
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| Online Access: | http://genominfo.org/upload/pdf/gi-2019-17-4-e41.pdf |
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| author | Seungyeoun Lee Heeju Lim |
| author_facet | Seungyeoun Lee Heeju Lim |
| author_sort | Seungyeoun Lee |
| collection | DOAJ |
| description | Survival analysis mainly deals with the time to event, including death, onset of disease, and bankruptcy. The common characteristic of survival analysis is that it contains “censored” data, in which the time to event cannot be completely observed, but instead represents the lower bound of the time to event. Only the occurrence of either time to event or censoring time is observed. Many traditional statistical methods have been effectively used for analyzing survival data with censored observations. However, with the development of high-throughput technologies for producing “omics” data, more advanced statistical methods, such as regularization, should be required to construct the predictive survival model with high-dimensional genomic data. Furthermore, machine learning approaches have been adapted for survival analysis, to fit nonlinear and complex interaction effects between predictors, and achieve more accurate prediction of individual survival probability. Presently, since most clinicians and medical researchers can easily assess statistical programs for analyzing survival data, a review article is helpful for understanding statistical methods used in survival analysis. We review traditional survival methods and regularization methods, with various penalty functions, for the analysis of high-dimensional genomics, and describe machine learning techniques that have been adapted to survival analysis. |
| format | Article |
| id | doaj-art-b203503cce944b859048bc510ca611d3 |
| institution | Kabale University |
| issn | 2234-0742 |
| language | English |
| publishDate | 2019-12-01 |
| publisher | BioMed Central |
| record_format | Article |
| series | Genomics & Informatics |
| spelling | doaj-art-b203503cce944b859048bc510ca611d32025-02-02T03:23:47ZengBioMed CentralGenomics & Informatics2234-07422019-12-0117410.5808/GI.2019.17.4.e41587Review of statistical methods for survival analysis using genomic dataSeungyeoun Lee0Heeju Lim1 Department of Mathematics and Statistics, Sejong University, Seoul 05006, Korea Department of Statistics, University of Connecticut, Storrs, CT 06269, USASurvival analysis mainly deals with the time to event, including death, onset of disease, and bankruptcy. The common characteristic of survival analysis is that it contains “censored” data, in which the time to event cannot be completely observed, but instead represents the lower bound of the time to event. Only the occurrence of either time to event or censoring time is observed. Many traditional statistical methods have been effectively used for analyzing survival data with censored observations. However, with the development of high-throughput technologies for producing “omics” data, more advanced statistical methods, such as regularization, should be required to construct the predictive survival model with high-dimensional genomic data. Furthermore, machine learning approaches have been adapted for survival analysis, to fit nonlinear and complex interaction effects between predictors, and achieve more accurate prediction of individual survival probability. Presently, since most clinicians and medical researchers can easily assess statistical programs for analyzing survival data, a review article is helpful for understanding statistical methods used in survival analysis. We review traditional survival methods and regularization methods, with various penalty functions, for the analysis of high-dimensional genomics, and describe machine learning techniques that have been adapted to survival analysis.http://genominfo.org/upload/pdf/gi-2019-17-4-e41.pdfcensoringcox modelkaplan-meier curvemachine learningregularizationsurvival time |
| spellingShingle | Seungyeoun Lee Heeju Lim Review of statistical methods for survival analysis using genomic data Genomics & Informatics censoring cox model kaplan-meier curve machine learning regularization survival time |
| title | Review of statistical methods for survival analysis using genomic data |
| title_full | Review of statistical methods for survival analysis using genomic data |
| title_fullStr | Review of statistical methods for survival analysis using genomic data |
| title_full_unstemmed | Review of statistical methods for survival analysis using genomic data |
| title_short | Review of statistical methods for survival analysis using genomic data |
| title_sort | review of statistical methods for survival analysis using genomic data |
| topic | censoring cox model kaplan-meier curve machine learning regularization survival time |
| url | http://genominfo.org/upload/pdf/gi-2019-17-4-e41.pdf |
| work_keys_str_mv | AT seungyeounlee reviewofstatisticalmethodsforsurvivalanalysisusinggenomicdata AT heejulim reviewofstatisticalmethodsforsurvivalanalysisusinggenomicdata |