scholarly journals Bayesian Bent-Cable Tobit Models for Longitudinal and Survival Data: Application to AIDS Studies

2018 ◽  
Vol 3 (1) ◽  
pp. 1-10
Author(s):  
Getachew A Dagne
Keyword(s):  
Survival Data ◽  
Tobit Models ◽  
Data Application ◽  
Longitudinal And Survival Data

scholarly journals Two-stage model for multivariate longitudinal and survival data with application to nephrology research

2017 ◽  
Vol 59 (6) ◽  
pp. 1204-1220 ◽  
Author(s):  
Ipek Guler ◽  
Christel Faes ◽  
Carmen Cadarso-Suárez ◽  
Laetitia Teixeira ◽  
Anabela Rodrigues ◽  
...  
Keyword(s):  
Survival Data ◽  
Stage Model ◽  
Two Stage ◽  
Longitudinal And Survival Data

scholarly journals Revisiting Methods For Modeling Longitudinal and Survival Data: The Framingham Heart Study

2021 ◽  
Author(s):  
Julius S Ngwa ◽  
Howard J Cabral ◽  
Debbie M Cheng ◽  
David R Gagnon ◽  
Michael P LaValley ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Longitudinal Data ◽  
Survival Data ◽  
Framingham Heart Study ◽  
Time Dependent ◽  
Time To Event ◽  
Longitudinal Measure ◽  
Heart Study ◽  
Longitudinal And Survival Data ◽  
Joint Method

Abstract Background: Statistical methods for modeling longitudinal and time-to-event data has received much attention in medical research and is becoming increasingly useful. In clinical studies, such as cancer and AIDS, longitudinal biomarkers are used to monitor disease progression and to predict survival. These longitudinal measures are often missing at failure times and may be prone to measurement errors. More importantly, time-dependent survival models that include the raw longitudinal measurements may lead to biased results. In previous studies these two types of data are frequently analyzed separately where a mixed effects model is used for the longitudinal data and a survival model is applied to the event outcome. Methods: In this paper we compare joint maximum likelihood methods, a two-step approach and a time dependent covariate method that link longitudinal data to survival data with emphasis on using longitudinal measures to predict survival. We apply a Bayesian semi-parametric joint method and maximum likelihood joint method that maximizes the joint likelihood of the time-to-event and longitudinal measures. We also implement the Two-Step approach, which estimates random effects separately, and a classic Time Dependent Covariate Model. We use simulation studies to assess bias, accuracy, and coverage probabilities for the estimates of the link parameter that connects the longitudinal measures to survival times. Results: Simulation results demonstrate that the Two-Step approach performed best at estimating the link parameter when variability in the longitudinal measure is low but is somewhat biased downwards when the variability is high. Bayesian semi-parametric and maximum likelihood joint methods yield higher link parameter estimates with low and high variability in the longitudinal measure. The Time Dependent Covariate method resulted in consistent underestimation of the link parameter. We illustrate these methods using data from the Framingham Heart Study in which lipid measurements and Myocardial Infarction data were collected over a period of 26 years.Conclusions: Traditional methods for modeling longitudinal and survival data, such as the time dependent covariate method, that use the observed longitudinal data, tend to provide downwardly biased estimates. The two-step approach and joint models provide better estimates, although a comparison of these methods may depend on the underlying residual variance.

Jointly Modelling Longitudinal and Survival Data

COMPSTAT ◽  
1998 ◽  
pp. 467-471
Author(s):  
Jeremy M. G. Taylor ◽  
Yan Wang
Keyword(s):  
Survival Data ◽  
Longitudinal And Survival Data

scholarly journals A new joint model for longitudinal and survival data with a cure fraction

2004 ◽  
Vol 91 (1) ◽  
pp. 18-34 ◽  
Author(s):  
Ming-Hui Chen ◽  
Joseph G Ibrahim ◽  
Debajyoti Sinha
Keyword(s):  
Survival Data ◽  
Joint Model ◽  
Cure Fraction ◽  
Longitudinal And Survival Data
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