scholarly journals Interval-Censored Time-to-Event Data: Methods and Applications By Ding-Geng (Din)ChenJianguoSunKarl E.PeaceBoca Raton, Florida: Chapman & Hall/CRC Press 2013 405 pages. UK £69.99 (hardback). ISBN 978-1-4665-0245-7

2015 ◽  
Vol 57 (1) ◽  
pp. 163-164
Author(s):  
Jun Ma
Keyword(s):  
Event Data ◽  
Time To Event ◽  
Time To Event Data ◽  
Interval Censored

Marginalized models for right-truncated and interval-censored time-to-event data

2019 ◽  
Vol 29 (6) ◽  
pp. 1043-1067
Author(s):  
Sammy Chebon ◽  
Christel Faes ◽  
Ann De Smedt ◽  
Helena Geys
Keyword(s):  
Event Data ◽  
Time To Event ◽  
Time To Event Data ◽  
Interval Censored ◽  
Marginalized Models

Flexible modelling of simultaneously interval censored and truncated time-to-event data

2015 ◽  
Vol 14 (4) ◽  
pp. 311-321 ◽  
Author(s):  
Sammy Chebon ◽  
Christel Faes ◽  
Ann De Smedt ◽  
Helena Geys
Keyword(s):  
Event Data ◽  
Time To Event ◽  
Time To Event Data ◽  
Interval Censored

Approximate Bayesian inference for joint linear and partially linear modeling of longitudinal zero-inflated count and time to event data

2021 ◽  
pp. 096228022110028
Author(s):  
T Baghfalaki ◽  
M Ganjali
Keyword(s):  
Linear Model ◽  
Joint Modeling ◽  
Partially Linear Model ◽  
Event Data ◽  
Time To Event ◽  
Data Set ◽  
Partially Linear ◽  
Time To Event Data ◽  
Count Response ◽  
Approximate Bayesian

Joint modeling of zero-inflated count and time-to-event data is usually performed by applying the shared random effect model. This kind of joint modeling can be considered as a latent Gaussian model. In this paper, the approach of integrated nested Laplace approximation (INLA) is used to perform approximate Bayesian approach for the joint modeling. We propose a zero-inflated hurdle model under Poisson or negative binomial distributional assumption as sub-model for count data. Also, a Weibull model is used as survival time sub-model. In addition to the usual joint linear model, a joint partially linear model is also considered to take into account the non-linear effect of time on the longitudinal count response. The performance of the method is investigated using some simulation studies and its achievement is compared with the usual approach via the Bayesian paradigm of Monte Carlo Markov Chain (MCMC). Also, we apply the proposed method to analyze two real data sets. The first one is the data about a longitudinal study of pregnancy and the second one is a data set obtained of a HIV study.

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