scholarly journals A tutorial for joint modeling of longitudinal and time-to-event data in R

2021 ◽  
Vol 1 ◽  
Author(s):  
Sezen Cekic ◽  
Stephen Aichele ◽  
Andreas M. Brandmaier ◽  
Ylva Köhncke ◽  
Paolo Ghisletta
Keyword(s):  
Joint Modeling ◽  
R Package ◽  
Model Parameters ◽  
Event Data ◽  
Discrete Events ◽  
Time To Event ◽  
Time To Event Data ◽  
Comparison Choice ◽  
Previous Decade ◽  
Behavioural Sciences

In biostatistics and medical research, longitudinal data are often composed of repeated assessments of a variable and dichotomous indicators to mark an event of interest. Consequently, joint modeling of longitudinal and time-to-event data has generated much interest in these disciplines over the previous decade. In behavioural sciences, too, often we are interested in relating individual trajectories and discrete events. Yet, joint modeling is rarely applied in behavioural sciences more generally. This tutorial presents an overview and general framework for joint modeling of longitudinal and time-to-event data, and fully illustrates its application in the context of a behavioral study with the JMbayes R package. In particular, the tutorial discusses practical topics, such as model selection and comparison, choice of joint modeling parameterization and interpretation of model parameters. In the end, this tutorial aims at introducing didactically the theory related to joint modeling and to introduce novice analysts to the use of the JMbayes package.

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.

scholarly journals Non-parametric frailty Cox models for hierarchical time-to-event data

Biostatistics ◽  
2018 ◽  
Vol 21 (3) ◽  
pp. 531-544 ◽  
Author(s):  
Francesca Gasperoni ◽  
Francesca Ieva ◽  
Anna Maria Paganoni ◽  
Christopher H Jackson ◽  
Linda Sharples
Keyword(s):  
Healthcare Providers ◽  
Discrete Distribution ◽  
Administrative Database ◽  
Model Parameters ◽  
Event Data ◽  
Time To Event ◽  
Cox Models ◽  
Time To Event Data ◽  
Parametric Frailty ◽  
Non Parametric

Summary We propose a novel model for hierarchical time-to-event data, for example, healthcare data in which patients are grouped by their healthcare provider. The most common model for this kind of data is the Cox proportional hazard model, with frailties that are common to patients in the same group and given a parametric distribution. We relax the parametric frailty assumption in this class of models by using a non-parametric discrete distribution. This improves the flexibility of the model by allowing very general frailty distributions and enables the data to be clustered into groups of healthcare providers with a similar frailty. A tailored Expectation–Maximization algorithm is proposed for estimating the model parameters, methods of model selection are compared, and the code is assessed in simulation studies. This model is particularly useful for administrative data in which there are a limited number of covariates available to explain the heterogeneity associated with the risk of the event. We apply the model to a clinical administrative database recording times to hospital readmission, and related covariates, for patients previously admitted once to hospital for heart failure, and we explore latent clustering structures among healthcare providers.

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

Estimating the Cutoff Points of Time-Dependent Risk Factors by Using Joint Modeling of Longitudinal and Time-to-Event Data: A 14-Year Follow-up Study—Tehran Lipid and Glucose Study

2019 ◽  
Vol 31 (8) ◽  
pp. 728-736
Author(s):  
Nezhat Shakeri ◽  
Fereidoun Azizi
Keyword(s):  
Risk Factors ◽  
Cox Model ◽  
Joint Modeling ◽  
Time Dependent ◽  
Estimation Methods ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data ◽  
Cutoff Points

Diagnostic accuracy and optimal cutoff points of risk factors is one of the important issues in medical decisions. In order to reassess the cutoff points of markers, longitudinal and time-to-event data of elderly individuals were collected repeatedly through 3 follow-up stages in the Tehran Lipid and Glucose Study. Time-dependent area under the ROC (receiver operating characteristic) curves (AUCs) based on the joint modeling of longitudinal and time-to-event data technique were measured. AUCs were considered to evaluate the discriminative potential of the models. The joint model produced higher AUC values than the Cox model; therefore, accuracy was improved although it is computationally complicated. The results had some differences with the thresholds reported in guidelines due to specificity to the population and/or the means of estimation methods. The estimated cutoff points with regard to sex can be used as a guideline for the Iranian elderly population.

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