Flexible link functions in a joint model of binary and longitudinal data

Stat ◽  
2015 ◽  
Vol 4 (1) ◽  
pp. 320-330 ◽  
Author(s):  
Dan Li ◽  
Xia Wang ◽  
Seongho Song ◽  
Nanhua Zhang ◽  
Dipak K. Dey
Keyword(s):  
Longitudinal Data ◽  
Joint Model ◽  
Flexible Link ◽  
Link Functions

Joint model for left-censored longitudinal data, recurrent events and terminal event: Predictive abilities of tumor burden for cancer evolution with application to the FFCD 2000-05 trial

Biometrics ◽  
2016 ◽  
Vol 72 (3) ◽  
pp. 907-916 ◽  
Author(s):  
Agnieszka Król ◽  
Loïc Ferrer ◽  
Jean-Pierre Pignon ◽  
Cécile Proust-Lima ◽  
Michel Ducreux ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Recurrent Events ◽  
Joint Model ◽  
Tumor Burden ◽  
Terminal Event ◽  
Cancer Evolution

scholarly journals A joint model for mixed and truncated longitudinal data and survival data, with application to HIV vaccine studies

Biostatistics ◽  
2017 ◽  
Vol 19 (3) ◽  
pp. 374-390 ◽  
Author(s):  
Tingting Yu ◽  
Lang Wu ◽  
Peter B Gilbert
Keyword(s):  
Longitudinal Data ◽  
Survival Data ◽  
Joint Model ◽  
Hiv Vaccine ◽  
Likelihood Inference ◽  
Likelihood Method ◽  
Parameter Estimates ◽  
Computationally Efficient ◽  
Approximate Likelihood ◽  
Longitudinal And Survival Data

SUMMARY In HIV vaccine studies, a major research objective is to identify immune response biomarkers measured longitudinally that may be associated with risk of HIV infection. This objective can be assessed via joint modeling of longitudinal and survival data. Joint models for HIV vaccine data are complicated by the following issues: (i) left truncations of some longitudinal data due to lower limits of quantification; (ii) mixed types of longitudinal variables; (iii) measurement errors and missing values in longitudinal measurements; (iv) computational challenges associated with likelihood inference. In this article, we propose a joint model of complex longitudinal and survival data and a computationally efficient method for approximate likelihood inference to address the foregoing issues simultaneously. In particular, our model does not make unverifiable distributional assumptions for truncated values, which is different from methods commonly used in the literature. The parameters are estimated based on the h-likelihood method, which is computationally efficient and offers approximate likelihood inference. Moreover, we propose a new approach to estimate the standard errors of the h-likelihood based parameter estimates by using an adaptive Gauss–Hermite method. Simulation studies show that our methods perform well and are computationally efficient. A comprehensive data analysis is also presented.

A Joint Model for Survival and Longitudinal Data Measured with Error

Biometrics ◽  
1997 ◽  
Vol 53 (1) ◽  
pp. 330 ◽  
Author(s):  
Michael S. Wulfsohn ◽  
Anastasios A. Tsiatis
Keyword(s):  
Longitudinal Data ◽  
Joint Model

scholarly journals Joint Modeling of a Longitudinal Measurement and Parametric Survival Data with Application to Primary Biliary Cirrhosis Study

2020 ◽  
pp. 295-304
Author(s):  
Elif Dil ◽  
Duru Karasoy
Keyword(s):  
Longitudinal Data ◽  
Regression Model ◽  
Survival Data ◽  
Cox Regression ◽  
Joint Model ◽  
Linear Mixed Effect Model ◽  
Separate Analysis ◽  
Biliary Cirrhosis ◽  
Mixed Effect ◽  
Cox Regression Model

Although longitudinal and survival data are collected in the same study, they are usually analyzed separately. Measurement errors and missing data problems arise because of separate analysis of these two data. Therefore, joint model should be used instead of separate analysis. The standard joint model frequently used in the literature is obtained by combining the linear mixed effect model of longitudinal data and Cox regression model with survival data. Nevertheless, to use the Cox regression model for survival data, the assumption of proportional hazards must be provided. Parametric survival sub-models should be used instead of the Cox regression model for the survival sub-model of the JM where the assumption is not provided. In this article, parametric joint modeling of longitudinal data and survival data that do not provide the assumption of proportional hazards are investigated. For the survival data, the model with Exponential, Weibull, Log-normal, Log-logistic, and Gamma accelerated failure time models and the linear mixed effect model are combined with random effects and the models were applied in primary biliary cirrhosis data set obtained from Mayo Clinic. After determining the best parametric joint model according to Akaike and Bayesian information criterions, the best available model was compared with standard joint model and of separate analysis of survival data and longitudinal data. As a results, in the studies where longitudinal and survival data are obtained together, it is seen that the parametric joint model gives more better results than the standard joint model when the proportional hazard assumption is not provided.

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