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Author:Suikkanen, Saara
Title:A review of Cox survival analysis using Gaussian processes
Katsaus Coxin selviytymismalleihin Gaussisia prosesseja käyttäen
Publication type:Master's thesis
Publication year:2013
Pages:viii + 54      Language:   eng
Department/School:Lääketieteellisen tekniikan ja laskennallisen tieteen laitos
Main subject:Laskennallinen ja kognitiivinen biotiede   (IL3003)
Supervisor:Lampinen, Jouko
Instructor:Vehtari, Aki
Electronic version URL: http://urn.fi/URN:NBN:fi:aalto-201304231907
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Location:P1 Ark Aalto  1489   | Archive
Keywords:bayesian calculation
survival models
gaussian processes
biomarkers
Cox survival model
ROC-curve
integrated discriminative improvement
log-predictive likelihood
bayesilainen laskenta
selviytymismallit
gaussiset prosessit
biomarkkerit
Cox:in selviytymismalli
ROC-kurva
IDI
log-prediktiivinen tiheys
Abstract (eng):Survival-models are widely used modeling tools among medical science.
The aim of using these models is to model the life time of observed item after some procedure in secondary prevention and the first onset of the event in primary prevention.
The models used for survival modeling are usually Cox survival model an Kaplan-Meier estimate.
The onset of these models is that they lack the time dependency model which obviously do exist between covariates and observed items.
The reason for this is that it has been really hard to interpret the time dependency model in to these models.
In this thesis I review the original Cox survival model and Kaplan-Meier estimate and review a new way to construct a survival model based on Cox survival model, Bayesian data analysis, and Gaussian processes.
In this way the time dependency model can be added to the survival model and the modeling accuracy improves at least on some level depending on what modeling accuracy measures we are using.
Abstract (fin):Selvitytymismallit ovat paljon käytettyjä mallinnusmenetelmiä lääketieteen parissa.
Näiden mallien avulla pyritän mallintamaan elinajan odotetta sekundaaripreventiotapauksessa tapahtuman jälkeistä selviytymistä tutkittaessa tai primaaripreventiotapauksessa tutkittaessa ajan jaksoa ensimmäisen tapahtuman ilmentymiseen.
Perinteisesti tähän tarkoitukseen ollaan käytetty Cox:in selviytymismalleja ja Kaplan-Meier estimaatteja.
Näiden mallien puutteena on kuitenkin selittävien muuttujien aikariippuvuuden mallintaminen puuttuminen.
Yleisessä tiedossa on, että aikariippuvuuksia on olemassa, näiden mallintaminen on vain hyvin hankalaa.
Tässä työssä kertaan perinteisen Coxin selviytymis-mallin sekä Kaplan-Maierin estimaatin sekä tavan rakentaa selviytymismalli Coxin mallin pohjalta, joka on toteutettu Bayesilaisen laskennan ja Gaussisten prosessien avulla.
Näiden menetelmien avulla malli aikariippuvuudelle saadaan mukaan survival-malliin ja mallin tarkkuus paranee verrattuna perinteiseen Coxin selviytymismalliin jonkin verran validointikriteeristä riippuen.
ED:2013-05-21
INSSI record number: 46731
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