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Bayesian joint modelling of longitudinal and time to event data: a methodological review Maya AlsefriAB×, Maria Stella, Marta GarcaFianaA, Kuwaiti KolamunnageDonaADepartment of Health Data Science,
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Bayesian joint modelling refers to a statistical technique that combines multiple sources of data or multiple modeling approaches to improve the understanding of complex phenomena by jointly estimating parameters.
Typically, researchers or statisticians conducting studies that require the use of joint models in the Bayesian framework are required to file Bayesian joint modelling, especially in academic or clinical research contexts.
Filling out Bayesian joint modelling involves specifying the joint distribution for the variables of interest, choosing appropriate priors, and using statistical software to implement the model to analyze the data.
The purpose of Bayesian joint modelling is to simultaneously account for different types of data and their relationships, allowing for better inference and predictions in complex models.
Information that must be reported includes the model specification, prior information, data sources, estimation methods, and results of the analysis including posterior distributions.
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