scholarly journals Quantifying and estimating the predictive accuracy for censored time-to-event data with competing risks

2018 ◽  
Vol 37 (21) ◽  
pp. 3106-3124 ◽  
Author(s):  
Cai Wu ◽  
Liang Li
Keyword(s):  
Competing Risks ◽  
Predictive Accuracy ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data

scholarly journals Modeling potential time to event data with competing risks

2013 ◽  
Vol 20 (2) ◽  
pp. 316-334 ◽  
Author(s):  
Liang Li ◽  
Bo Hu ◽  
Michael W. Kattan
Keyword(s):  
Competing Risks ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data

scholarly journals Marginal Models for Clustered Time-to-Event Data with Competing Risks Using Pseudovalues

Biometrics ◽  
2010 ◽  
Vol 67 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Brent R. Logan ◽  
Mei-Jie Zhang ◽  
John P. Klein
Keyword(s):  
Competing Risks ◽  
Event Data ◽  
Marginal Models ◽  
Time To Event ◽  
Time To Event Data

scholarly journals Boosting for high-dimensional time-to-event data with competing risks

2009 ◽  
Vol 25 (7) ◽  
pp. 890-896 ◽  
Author(s):  
H. Binder ◽  
A. Allignol ◽  
M. Schumacher ◽  
J. Beyersmann
Keyword(s):  
Competing Risks ◽  
High Dimensional ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data

Number needed to treat for time-to-event data with competing risks

2013 ◽  
Vol 33 (2) ◽  
pp. 181-192 ◽  
Author(s):  
Natalia A. Gouskova ◽  
Suprateek Kundu ◽  
Peter B. Imrey ◽  
Jason P. Fine
Keyword(s):  
Competing Risks ◽  
Number Needed To Treat ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data

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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