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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Fully nonparametric regression modelling of misclassified censored time-to-event data
Original language description
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.
Czech name
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Czech description
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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