Fully nonparametric regression modelling of misclassified censored time-to-event data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F15%3A10312680" target="_blank" >RIV/00216208:11320/15:10312680 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-19518-6" target="_blank" >http://dx.doi.org/10.1007/978-3-319-19518-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-19518-6" target="_blank" >10.1007/978-3-319-19518-6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fully nonparametric regression modelling of misclassified censored time-to-event data

Popis výsledku v původním jazyce

We propose a fully nonparametric modelling approach for time-to-event regression data, when the response of interest can only be determined to lie in an interval obtained from a sequence of examination times and the determination of the occurrence of theevent is subject to misclassification. The covariate-dependent time-to-event distributions are modelled using a linear dependent Dirichlet process mixture model. A general misclassification model is discussed, considering the possibility that differentexaminers were involved in the assessment of the occurrence of the events for a given subject across time. An advantage of the proposed model is that the underlying time-to-event distributions and the misclassification parameters can be estimated withoutany external information about the latter parameters.

Název v anglickém jazyce

Fully nonparametric regression modelling of misclassified censored time-to-event data

Popis výsledku anglicky

We propose a fully nonparametric modelling approach for time-to-event regression data, when the response of interest can only be determined to lie in an interval obtained from a sequence of examination times and the determination of the occurrence of theevent is subject to misclassification. The covariate-dependent time-to-event distributions are modelled using a linear dependent Dirichlet process mixture model. A general misclassification model is discussed, considering the possibility that differentexaminers were involved in the assessment of the occurrence of the events for a given subject across time. An advantage of the proposed model is that the underlying time-to-event distributions and the misclassification parameters can be estimated withoutany external information about the latter parameters.

Druh

C - Kapitola v odborné knize

CEP obor

BA - Obecná matematika

OECD FORD obor

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Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

Nonparametric Bayesian Inference in Biostatistics

ISBN

978-3-319-19517-9

Počet stran výsledku

21

Strana od-do

247-267

Počet stran knihy

465

Název nakladatele

Springer

Místo vydání

Cham

Kód UT WoS kapitoly

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