H-likelihood approach for joint modeling of longitudinal outcomes and time-to-event data

2017 ◽  
Vol 59 (6) ◽  
pp. 1122-1143 ◽  
Author(s):  
Il Do Ha ◽  
Maengseok Noh ◽  
Youngjo Lee
Keyword(s):  
Joint Modeling ◽  
Event Data ◽  
Time To Event ◽  
Longitudinal Outcomes ◽  
Time To Event Data ◽  
Likelihood Approach

A Semiparametric Likelihood Approach to Joint Modeling of Longitudinal and Time-to-Event Data

Biometrics ◽  
2002 ◽  
Vol 58 (4) ◽  
pp. 742-753 ◽  
Author(s):  
Xiao Song ◽  
Marie Davidian ◽  
Anastasios A. Tsiatis
Keyword(s):  
Joint Modeling ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data ◽  
Likelihood Approach ◽  
Semiparametric Likelihood

An Overview of Joint Modeling of Time-to-Event and Longitudinal Outcomes

2019 ◽  
Vol 6 (1) ◽  
pp. 223-240 ◽  
Author(s):  
Grigorios Papageorgiou ◽  
Katya Mauff ◽  
Anirudh Tomer ◽  
Dimitris Rizopoulos
Keyword(s):  
Joint Modeling ◽  
Joint Model ◽  
Event Data ◽  
Time To Event ◽  
Joint Models ◽  
Longitudinal Outcomes ◽  
Estimation Techniques ◽  
Time To Event Data ◽  
Association Structure

In this review, we present an overview of joint models for longitudinal and time-to-event data. We introduce a generalized formulation for the joint model that incorporates multiple longitudinal outcomes of varying types. We focus on extensions for the parametrization of the association structure that links the longitudinal and time-to-event outcomes, estimation techniques, and dynamic predictions. We also outline the software available for the application of these models.

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.

Joint modeling of longitudinal and time-to-event data on multivariate protein biomarkers

2021 ◽  
Vol 381 ◽  
pp. 113016
Author(s):  
Abin Thomas ◽  
Gajendra K. Vishwakarma ◽  
Atanu Bhattacharjee
Keyword(s):  
Joint Modeling ◽  
Event Data ◽  
Time To Event ◽  
Protein Biomarkers ◽  
Time To Event Data

scholarly journals A flexible joint modeling framework for longitudinal and time-to-event data with overdispersion

2016 ◽  
Vol 25 (4) ◽  
pp. 1661-1676 ◽  
Author(s):  
Edmund N Njagi ◽  
Geert Molenberghs ◽  
Dimitris Rizopoulos ◽  
Geert Verbeke ◽  
Michael G Kenward ◽  
...  
Keyword(s):  
Joint Modeling ◽  
Event Data ◽  
Time To Event ◽  
Modeling Framework ◽  
Flexible Joint ◽  
Time To Event Data

FITTING JOINT MODELING OF LONGITUDINAL AND TIME-TO-EVENT DATA USING STOCHASTIC EM APPROACH

2020 ◽  
Vol 64 (1) ◽  
pp. 33-62
Author(s):  
Dina M. Sabry ◽  
Ahmed M. Gad ◽  
Ramadan H. Mohamed
Keyword(s):  
Joint Modeling ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data ◽  
Stochastic Em

Joint Modeling of Longitudinal and Time-to-Event Data

2016 ◽  
Author(s):  
Robert Elashoff
Keyword(s):  
Joint Modeling ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data

Joint modeling of multivariate longitudinal mixed measurements and time to event data using a Bayesian approach

2014 ◽  
Vol 41 (9) ◽  
pp. 1934-1955 ◽  
Author(s):  
T. Baghfalaki ◽  
M. Ganjali ◽  
D. Berridge
Keyword(s):  
Bayesian Approach ◽  
Joint Modeling ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data
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