Robust joint modelling of longitudinal and survival data: Incorporating a time‐varying degrees‐of‐freedom parameter

2021 ◽  
Author(s):  
Lisa M. McFetridge ◽  
Özgür Asar ◽  
Jonas Wallin
Keyword(s):  
Survival Data ◽  
Degrees Of Freedom ◽  
Time Varying ◽  
Joint Modelling ◽  
Longitudinal And Survival Data

scholarly journals Flexible parametric joint modelling of longitudinal and survival data

2012 ◽  
Vol 31 (30) ◽  
pp. 4456-4471 ◽  
Author(s):  
Michael J. Crowther ◽  
Keith R. Abrams ◽  
Paul C. Lambert
Keyword(s):  
Survival Data ◽  
Joint Modelling ◽  
Longitudinal And Survival Data

scholarly journals JMcmprsk: An R Package for Joint Modelling of Longitudinal and Survival Data with Competing Risks

The R Journal ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 53
Author(s):  
Hong Wang ◽  
Ning Li ◽  
Shanpeng Li ◽  
Gang Li
Keyword(s):  
Competing Risks ◽  
Survival Data ◽  
R Package ◽  
Joint Modelling ◽  
Longitudinal And Survival Data

scholarly journals Assessing importance of biomarkers: A Bayesian joint modelling approach of longitudinal and survival data with semi-competing risks

2020 ◽  
pp. 1471082X2093336
Author(s):  
Fan Zhang ◽  
Ming-Hui Chen ◽  
Xiuyu Julie Cong ◽  
Qingxia Chen
Keyword(s):  
Disease Progression ◽  
Competing Risks ◽  
Survival Data ◽  
Marginal Likelihood ◽  
Cox Proportional Hazards Model ◽  
Terminal Event ◽  
Joint Modelling ◽  
Patient Reported ◽  
Longitudinal And Survival Data ◽  
Time Varying Covariates

Longitudinal biomarkers such as patient-reported outcomes (PROs) and quality of life (QOL) are routinely collected in cancer clinical trials or other studies. Joint modelling of PRO/QOL and survival data can provide a comparative assessment of patient-reported changes in specific symptoms or global measures that correspond to changes in survival. Motivated by a head and neck cancer clinical trial, we develop a class of trajectory-based models for longitudinal and survival data with disease progression. Specifically, we propose a class of mixed effects regression models for longitudinal measures, a cure rate model for the disease progression time ([Formula: see text]) and a Cox proportional hazards model with time-varying covariates for the overall survival time ([Formula: see text]) to account for [Formula: see text] and treatment switching. Under the semi-competing risks framework, the disease progression is the non-terminal event, the occurrence of which is subject to a terminal event of death. The properties of the proposed models are examined in detail. Within the Bayesian paradigm, we derive the decompositions of the deviance information criterion (DIC) and the logarithm of the pseudo-marginal likelihood (LPML) to assess the fit of the longitudinal component of the model and the fit of each survival component, separately. We further develop [Formula: see text]DIC as well as [Formula: see text]LPML to determine the importance and contribution of the longitudinal data to the model fit of the [Formula: see text] and [Formula: see text] data.

scholarly journals Improved dynamic predictions from joint models of longitudinal and survival data with time-varying effects using P-splines

Biometrics ◽  
2017 ◽  
Vol 74 (2) ◽  
pp. 685-693 ◽  
Author(s):  
Eleni-Rosalina Andrinopoulou ◽  
Paul H. C. Eilers ◽  
Johanna J. M. Takkenberg ◽  
Dimitris Rizopoulos
Keyword(s):  
Survival Data ◽  
Time Varying ◽  
Joint Models ◽  
Longitudinal And Survival Data
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