Bayesian joint modeling of ordinal longitudinal measurements and competing risks survival data for analysing Tehran Lipid and Glucose Study

2020 ◽  
Vol 30 (4) ◽  
pp. 689-703
Author(s):  
Taban Baghfalaki ◽  
Shiva Kalantari ◽  
Mojtaba Ganjali ◽  
Farzad Hadaegh ◽  
Bagher Pahlavanzadeh
Keyword(s):  
Competing Risks ◽  
Survival Data ◽  
Joint Modeling ◽  
Longitudinal Measurements

scholarly journals Joint Modeling of Longitudinal Measurements and Multiple Failure Time Using Fully-specified Subdistribution Model: A Bayesian Perspective

2020 ◽  
Author(s):  
Fatemeh Sadat Hosseini-Baharanchi ◽  
Ahmad Reza Baghestani ◽  
Taban Baghfalaki ◽  
Ebrahim Hajizadeh ◽  
Katayoun Najafizadeh ◽  
...  
Keyword(s):  
Competing Risks ◽  
Simulation Study ◽  
Lung Transplant ◽  
Failure Time ◽  
Random Effect ◽  
Joint Modeling ◽  
Mixed Effect ◽  
Bayesian Paradigm ◽  
Longitudinal Measurements ◽  
Proposed Model

In biomedical studies, competing risks framework in which an individual fails due to multiple causes is frequently available. Joint modeling of longitudinal measurements and competing risks has become prominent, recently. In this paper, we proposed a joint model considering fully- specified subdistribution model introduced by Ge and Chen (2012) and longitudinal measurements. The proposed model links a linear mixed effect submodel to a fully-specified subdistribution submodel through a shared random effect. A Bayesian paradigm using MCMC is adopted to estimate the parameters. Performance of the proposed model is illustrated using a simulation study. In addition, this model is used to analyze the lung transplant dataset.

A Practical Guide to Family Studies with Lifetime Data

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Thomas H. Scheike ◽  
Klaus Kähler Holst
Keyword(s):  
Competing Risks ◽  
Survival Data ◽  
Age Of Onset ◽  
Familial Aggregation ◽  
Threshold Model ◽  
Right Censoring ◽  
Annual Review ◽  
Publication Date ◽  
Lifetime Data ◽  
Competing Risks Data

Familial aggregation refers to the fact that a particular disease may be overrepresented in some families due to genetic or environmental factors. When studying such phenomena, it is clear that one important aspect is the age of onset of the disease in question, and in addition, the data will typically be right-censored. Therefore, one must apply lifetime data methods to quantify such dependence and to separate it into different sources using polygenic modeling. Another important point is that the occurrence of a particular disease can be prevented by death—that is, competing risks—and therefore, the familial aggregation should be studied in a model that allows for both death and the occurrence of the disease. We here demonstrate how polygenic modeling can be done for both survival data and competing risks data dealing with right-censoring. The competing risks modeling that we focus on is closely related to the liability threshold model. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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