A flexible AFT model for misclassified clustered interval-censored data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F16%3A10328772" target="_blank" >RIV/00216208:11320/16:10328772 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1111/biom.12424" target="_blank" >http://dx.doi.org/10.1111/biom.12424</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/biom.12424" target="_blank" >10.1111/biom.12424</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A flexible AFT model for misclassified clustered interval-censored data

Popis výsledku v původním jazyce

Motivated by a longitudinal oral health study, we propose a flexible modeling approach for clustered time-to-event data, when the response of interest can only be determined to lie in an interval obtained from asequence of examination times (interval-censored data) and on top of that, the determination of the occurrence of the event is subject to misclassification. The clustered time-to-event data are modeled using an accelerated failure time model with random effects and by assuming a penalized Gaussian mixture model for the random effects terms to avoid restrictive distributional assumptions concerning the event times. A general misclassification model is discussed in detail, considering the possibility that different examiners were involved in the assessment of the occurrence of the events for agiven subject across time. A Bayesian implementation of the proposed model is described in a detailed manner. We additionally provide empirical evidence showing that the model can be used to estimate the underlying time-to-event distribution and the misclassification parameters without any external information about the latter parameters. We also provide results of asimulation study to evaluate the effect of neglecting the presence of misclassification in the analysis of clustered time-to-event data.

Název v anglickém jazyce

A flexible AFT model for misclassified clustered interval-censored data

Popis výsledku anglicky

Motivated by a longitudinal oral health study, we propose a flexible modeling approach for clustered time-to-event data, when the response of interest can only be determined to lie in an interval obtained from asequence of examination times (interval-censored data) and on top of that, the determination of the occurrence of the event is subject to misclassification. The clustered time-to-event data are modeled using an accelerated failure time model with random effects and by assuming a penalized Gaussian mixture model for the random effects terms to avoid restrictive distributional assumptions concerning the event times. A general misclassification model is discussed in detail, considering the possibility that different examiners were involved in the assessment of the occurrence of the events for agiven subject across time. A Bayesian implementation of the proposed model is described in a detailed manner. We additionally provide empirical evidence showing that the model can be used to estimate the underlying time-to-event distribution and the misclassification parameters without any external information about the latter parameters. We also provide results of asimulation study to evaluate the effect of neglecting the presence of misclassification in the analysis of clustered time-to-event data.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

—

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název periodika

Biometrics

ISSN

0006-341X

e-ISSN

—

Svazek periodika

72

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

473-483

Kód UT WoS článku

000378527900017

EID výsledku v databázi Scopus

2-s2.0-84979052587