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Statements

Subject Item
n2:RIV%2F67985807%3A_____%2F13%3A00395915%21RIV14-AV0-67985807
rdf:type
n6:Vysledek skos:Concept
dcterms:description
Survival analysis is concerned with analyzing time-to-event data where the event of interest usually represents some type of “failure. In clinical medicine, the event of interest may be e.g. death of a patient from well specified causes, autoimmune rejection of the graft by the transplant recipient or other type of graft failure in transplant studies. In certain situations, however, the true survival outcomes may not be observable, because we have observed a so called “censoring event which prevented the event of interest from occurring. Such censoring event may represent, for instance, loss of a particular subject from follow-up, occurrence of administrative censoring, which typically takes place in clinical trials, or we may indeed observe other type of “failure, e.g. death from fatal injuries rather than from cardiovascular causes which were of primary interest in a particular clinical trial. In this article we will stress the importance of a key assumption relating censoring process to survival outcomes and review principle univariate survival analysis methods for uncorrelated data. We will review popular models for analyzing univariate survival data, many of which enable us quantifying effect the prognostic variables independently exert on survival outcomes. Model examples will cover the classes of non-parametric, parametric and semi-parametric methods. We will also review underlying assumptions of individual models and stress the importance of using appropriate models in analyzing univariate time-to-event data. Survival analysis is concerned with analyzing time-to-event data where the event of interest usually represents some type of “failure. In clinical medicine, the event of interest may be e.g. death of a patient from well specified causes, autoimmune rejection of the graft by the transplant recipient or other type of graft failure in transplant studies. In certain situations, however, the true survival outcomes may not be observable, because we have observed a so called “censoring event which prevented the event of interest from occurring. Such censoring event may represent, for instance, loss of a particular subject from follow-up, occurrence of administrative censoring, which typically takes place in clinical trials, or we may indeed observe other type of “failure, e.g. death from fatal injuries rather than from cardiovascular causes which were of primary interest in a particular clinical trial. In this article we will stress the importance of a key assumption relating censoring process to survival outcomes and review principle univariate survival analysis methods for uncorrelated data. We will review popular models for analyzing univariate survival data, many of which enable us quantifying effect the prognostic variables independently exert on survival outcomes. Model examples will cover the classes of non-parametric, parametric and semi-parametric methods. We will also review underlying assumptions of individual models and stress the importance of using appropriate models in analyzing univariate time-to-event data.
dcterms:title
Introduction to Survival Analysis Introduction to Survival Analysis
skos:prefLabel
Introduction to Survival Analysis Introduction to Survival Analysis
skos:notation
RIV/67985807:_____/13:00395915!RIV14-AV0-67985807
n6:predkladatel
n7:ico%3A67985807
n3:aktivita
n16:I
n3:aktivity
I
n3:dodaniDat
n8:2014
n3:domaciTvurceVysledku
n14:6423205
n3:druhVysledku
n12:D
n3:duvernostUdaju
n19:S
n3:entitaPredkladatele
n15:predkladatel
n3:idSjednocenehoVysledku
81016
n3:idVysledku
RIV/67985807:_____/13:00395915
n3:jazykVysledku
n10:eng
n3:klicovaSlova
survival analysis; time-to-event data; censoring process; hazard function; survival time
n3:klicoveSlovo
n4:survival%20analysis n4:survival%20time n4:time-to-event%20data n4:hazard%20function n4:censoring%20process
n3:kontrolniKodProRIV
[FF319E94CB08]
n3:mistoKonaniAkce
Svratka
n3:mistoVydani
Brno
n3:nazevZdroje
Proceedings of the 9th Summer School in Computational Biology. Stochastic Modelling in Epidemiology
n3:obor
n5:IN
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
1
n3:rokUplatneniVysledku
n8:2013
n3:tvurceVysledku
Valenta, Zdeněk
n3:typAkce
n20:CST
n3:zahajeniAkce
2013-09-10+02:00
s:numberOfPages
13
n9:hasPublisher
Masarykova univerzita
n17:isbn
978-80-210-6305-1