Survival analysis: part II – applied clinical data analysis
As a follow-up to a previous article, this review provides several in-depth concepts regarding a survival analysis. Also, several codes for specific survival analysis are listed to enhance the understanding of such an analysis and to provide an applicable survival analysis method. A proportional haz...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Korean Society of Anesthesiologists
2019-10-01
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| Series: | Korean Journal of Anesthesiology |
| Subjects: | |
| Online Access: | http://ekja.org/upload/pdf/kja-19183.pdf |
| _version_ | 1819265302100180992 |
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| author | Junyong In Dong Kyu Lee |
| author_facet | Junyong In Dong Kyu Lee |
| author_sort | Junyong In |
| collection | DOAJ |
| description | As a follow-up to a previous article, this review provides several in-depth concepts regarding a survival analysis. Also, several codes for specific survival analysis are listed to enhance the understanding of such an analysis and to provide an applicable survival analysis method. A proportional hazard assumption is an important concept in survival analysis. Validation of this assumption is crucial for survival analysis. For this purpose, a graphical analysis method and a goodnessof- fit test are introduced along with detailed codes and examples. In the case of a violated proportional hazard assumption, the extended models of a Cox regression are required. Simplified concepts of a stratified Cox proportional hazard model and time-dependent Cox regression are also described. The source code for an actual analysis using an available statistical package with a detailed interpretation of the results can enable the realization of survival analysis with personal data. To enhance the statistical power of survival analysis, an evaluation of the basic assumptions and the interaction between variables and time is important. In doing so, survival analysis can provide reliable scientific results with a high level of confidence. |
| first_indexed | 2024-12-23T20:43:13Z |
| format | Article |
| id | doaj.art-3cab4491a14e4efdb77d120aa477659c |
| institution | Directory Open Access Journal |
| issn | 2005-6419 2005-7563 |
| language | English |
| last_indexed | 2024-12-23T20:43:13Z |
| publishDate | 2019-10-01 |
| publisher | Korean Society of Anesthesiologists |
| record_format | Article |
| series | Korean Journal of Anesthesiology |
| spelling | doaj.art-3cab4491a14e4efdb77d120aa477659c2022-12-21T17:31:52ZengKorean Society of AnesthesiologistsKorean Journal of Anesthesiology2005-64192005-75632019-10-0172544145710.4097/kja.191838532Survival analysis: part II – applied clinical data analysisJunyong In0Dong Kyu Lee1 Department of Anesthesiology and Pain Medicine, Dongguk University Ilsan Hospital, Goyang, Korea Department of Anesthesiology and Pain Medicine, Guro Hospital, Korea University School of Medicine, Seoul, KoreaAs a follow-up to a previous article, this review provides several in-depth concepts regarding a survival analysis. Also, several codes for specific survival analysis are listed to enhance the understanding of such an analysis and to provide an applicable survival analysis method. A proportional hazard assumption is an important concept in survival analysis. Validation of this assumption is crucial for survival analysis. For this purpose, a graphical analysis method and a goodnessof- fit test are introduced along with detailed codes and examples. In the case of a violated proportional hazard assumption, the extended models of a Cox regression are required. Simplified concepts of a stratified Cox proportional hazard model and time-dependent Cox regression are also described. The source code for an actual analysis using an available statistical package with a detailed interpretation of the results can enable the realization of survival analysis with personal data. To enhance the statistical power of survival analysis, an evaluation of the basic assumptions and the interaction between variables and time is important. In doing so, survival analysis can provide reliable scientific results with a high level of confidence.http://ekja.org/upload/pdf/kja-19183.pdfcox regressionextended cox regressiongoodness of fit testlog minus log plotproportional hazard assumptionschoenfeld residualstratified cox regressionsurvival analysistime-dependent coefficienttime-dependent cox regression |
| spellingShingle | Junyong In Dong Kyu Lee Survival analysis: part II – applied clinical data analysis Korean Journal of Anesthesiology cox regression extended cox regression goodness of fit test log minus log plot proportional hazard assumption schoenfeld residual stratified cox regression survival analysis time-dependent coefficient time-dependent cox regression |
| title | Survival analysis: part II – applied clinical data analysis |
| title_full | Survival analysis: part II – applied clinical data analysis |
| title_fullStr | Survival analysis: part II – applied clinical data analysis |
| title_full_unstemmed | Survival analysis: part II – applied clinical data analysis |
| title_short | Survival analysis: part II – applied clinical data analysis |
| title_sort | survival analysis part ii applied clinical data analysis |
| topic | cox regression extended cox regression goodness of fit test log minus log plot proportional hazard assumption schoenfeld residual stratified cox regression survival analysis time-dependent coefficient time-dependent cox regression |
| url | http://ekja.org/upload/pdf/kja-19183.pdf |
| work_keys_str_mv | AT junyongin survivalanalysispartiiappliedclinicaldataanalysis AT dongkyulee survivalanalysispartiiappliedclinicaldataanalysis |