Kaplan-Meyer Survival Curves: Simulation Technique

The right censoring of survival data, being the most conventional method of research, is analyzed. The patient survival is explored in a time span that is shorter in fact than the actual survival time. However, when the actual survival time is unknown, the proxy of the observable survival time will...
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Autor*in:	H. HOLUBOVA [verfasserIn]

Format:	E-Artikel
Sprache:	Ukrainisch

Erschienen:	2021

Schlagwörter:	survival, survival curves, probability of survival, survival time, event

Übergeordnetes Werk:	In: Naukovij Vìsnik Nacìonalʹnoï Akademìï Statistiki, Oblìku ta Auditu - National Academy of Statistics, Accounting and Audit, 2017, (2021), 3-4, Seite 15-22
Übergeordnetes Werk:	year:2021 ; number:3-4 ; pages:15-22

Links:	Link aufrufen Link aufrufen Journal toc Journal toc

Katalog-ID:	DOAJ078885310

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520			\|a The right censoring of survival data, being the most conventional method of research, is analyzed. The patient survival is explored in a time span that is shorter in fact than the actual survival time. However, when the actual survival time is unknown, the proxy of the observable survival time will be used for estimating the actual survival time. The algorithm for estimation of survival probabilities is demonstrated by data on 20 patients during six months, with visualizing the technique of simulating Kaplan – Meyer curves by categorical variables (method of treatment and gender) using GraphPad Prism software for statistical data processing. It is argued that Kaplan – Meyer curves could provide an effective tool in simulating the patient survival in case of COVID-19 by various criteria of grouping: gender (male and female); treatment method; associated diseases (diabetes and others); age group; vaccinated or not vaccinated patients etc. The significance of differences between survival curves of patienst in various groups can be found using Log-Rank test, Gehan – Wilcoxon test, Mantel – Cox test and others. The results of tests produced on the basis of data on 42 patients ill with leukemia show significant differences in the survival between two groups of patients. This confirms the assumption that the new method of treatment is more effective than the conventional one. The main deficiency of the nonparametric method of Kaplan – Meyer is that it is incapable to build curves by several categorical variables. The main advantages of Cox regression based on the Cox proportional hazards model are demonstrated.
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allfieldsSound	(DE-627)DOAJ078885310 (DE-599)DOAJb7cc58fe362247e28525ba0eb58fe660 DE-627 ger DE-627 rakwb ukr HA1-4737 H. HOLUBOVA verfasserin aut Kaplan-Meyer Survival Curves: Simulation Technique 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The right censoring of survival data, being the most conventional method of research, is analyzed. The patient survival is explored in a time span that is shorter in fact than the actual survival time. However, when the actual survival time is unknown, the proxy of the observable survival time will be used for estimating the actual survival time. The algorithm for estimation of survival probabilities is demonstrated by data on 20 patients during six months, with visualizing the technique of simulating Kaplan – Meyer curves by categorical variables (method of treatment and gender) using GraphPad Prism software for statistical data processing. It is argued that Kaplan – Meyer curves could provide an effective tool in simulating the patient survival in case of COVID-19 by various criteria of grouping: gender (male and female); treatment method; associated diseases (diabetes and others); age group; vaccinated or not vaccinated patients etc. The significance of differences between survival curves of patienst in various groups can be found using Log-Rank test, Gehan – Wilcoxon test, Mantel – Cox test and others. The results of tests produced on the basis of data on 42 patients ill with leukemia show significant differences in the survival between two groups of patients. This confirms the assumption that the new method of treatment is more effective than the conventional one. The main deficiency of the nonparametric method of Kaplan – Meyer is that it is incapable to build curves by several categorical variables. The main advantages of Cox regression based on the Cox proportional hazards model are demonstrated. survival, survival curves, probability of survival, survival time, event Statistics In Naukovij Vìsnik Nacìonalʹnoï Akademìï Statistiki, Oblìku ta Auditu National Academy of Statistics, Accounting and Audit, 2017 (2021), 3-4, Seite 15-22 (DE-627)1760637386 25211323 nnns year:2021 number:3-4 pages:15-22 https://doaj.org/article/b7cc58fe362247e28525ba0eb58fe660 kostenfrei https://nasoa-journal.com.ua/index.php/journal/article/view/245 kostenfrei https://doaj.org/toc/2520-6834 Journal toc kostenfrei https://doaj.org/toc/2521-1323 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2021 3-4 15-22
language	Ukrainian
source	In Naukovij Vìsnik Nacìonalʹnoï Akademìï Statistiki, Oblìku ta Auditu (2021), 3-4, Seite 15-22 year:2021 number:3-4 pages:15-22
sourceStr	In Naukovij Vìsnik Nacìonalʹnoï Akademìï Statistiki, Oblìku ta Auditu (2021), 3-4, Seite 15-22 year:2021 number:3-4 pages:15-22
format_phy_str_mv	Article
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topic_facet	survival, survival curves, probability of survival, survival time, event Statistics
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HOLUBOVA</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Kaplan-Meyer Survival Curves: Simulation Technique</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The right censoring of survival data, being the most conventional method of research, is analyzed. 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title	Kaplan-Meyer Survival Curves: Simulation Technique
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title_auth	Kaplan-Meyer Survival Curves: Simulation Technique
abstract	The right censoring of survival data, being the most conventional method of research, is analyzed. The patient survival is explored in a time span that is shorter in fact than the actual survival time. However, when the actual survival time is unknown, the proxy of the observable survival time will be used for estimating the actual survival time. The algorithm for estimation of survival probabilities is demonstrated by data on 20 patients during six months, with visualizing the technique of simulating Kaplan – Meyer curves by categorical variables (method of treatment and gender) using GraphPad Prism software for statistical data processing. It is argued that Kaplan – Meyer curves could provide an effective tool in simulating the patient survival in case of COVID-19 by various criteria of grouping: gender (male and female); treatment method; associated diseases (diabetes and others); age group; vaccinated or not vaccinated patients etc. The significance of differences between survival curves of patienst in various groups can be found using Log-Rank test, Gehan – Wilcoxon test, Mantel – Cox test and others. The results of tests produced on the basis of data on 42 patients ill with leukemia show significant differences in the survival between two groups of patients. This confirms the assumption that the new method of treatment is more effective than the conventional one. The main deficiency of the nonparametric method of Kaplan – Meyer is that it is incapable to build curves by several categorical variables. The main advantages of Cox regression based on the Cox proportional hazards model are demonstrated.
abstractGer	The right censoring of survival data, being the most conventional method of research, is analyzed. The patient survival is explored in a time span that is shorter in fact than the actual survival time. However, when the actual survival time is unknown, the proxy of the observable survival time will be used for estimating the actual survival time. The algorithm for estimation of survival probabilities is demonstrated by data on 20 patients during six months, with visualizing the technique of simulating Kaplan – Meyer curves by categorical variables (method of treatment and gender) using GraphPad Prism software for statistical data processing. It is argued that Kaplan – Meyer curves could provide an effective tool in simulating the patient survival in case of COVID-19 by various criteria of grouping: gender (male and female); treatment method; associated diseases (diabetes and others); age group; vaccinated or not vaccinated patients etc. The significance of differences between survival curves of patienst in various groups can be found using Log-Rank test, Gehan – Wilcoxon test, Mantel – Cox test and others. The results of tests produced on the basis of data on 42 patients ill with leukemia show significant differences in the survival between two groups of patients. This confirms the assumption that the new method of treatment is more effective than the conventional one. The main deficiency of the nonparametric method of Kaplan – Meyer is that it is incapable to build curves by several categorical variables. The main advantages of Cox regression based on the Cox proportional hazards model are demonstrated.
abstract_unstemmed	The right censoring of survival data, being the most conventional method of research, is analyzed. The patient survival is explored in a time span that is shorter in fact than the actual survival time. However, when the actual survival time is unknown, the proxy of the observable survival time will be used for estimating the actual survival time. The algorithm for estimation of survival probabilities is demonstrated by data on 20 patients during six months, with visualizing the technique of simulating Kaplan – Meyer curves by categorical variables (method of treatment and gender) using GraphPad Prism software for statistical data processing. It is argued that Kaplan – Meyer curves could provide an effective tool in simulating the patient survival in case of COVID-19 by various criteria of grouping: gender (male and female); treatment method; associated diseases (diabetes and others); age group; vaccinated or not vaccinated patients etc. The significance of differences between survival curves of patienst in various groups can be found using Log-Rank test, Gehan – Wilcoxon test, Mantel – Cox test and others. The results of tests produced on the basis of data on 42 patients ill with leukemia show significant differences in the survival between two groups of patients. This confirms the assumption that the new method of treatment is more effective than the conventional one. The main deficiency of the nonparametric method of Kaplan – Meyer is that it is incapable to build curves by several categorical variables. The main advantages of Cox regression based on the Cox proportional hazards model are demonstrated.
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Kaplan-Meyer Survival Curves: Simulation Technique

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