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Activity Number:
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387
- Software
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Type:
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Contributed
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Date/Time:
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Thursday, August 12, 2021 : 12:00 PM to 1:50 PM
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Sponsor:
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Royal Statistical Society
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Abstract #319207
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Title:
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A Gaussian copula joint model for longitudinal and time to event data with random effects
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Author(s):
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Zili Zhang* and Christiana Charalambous and Peter Foster
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Companies:
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The University of Manchester and University of Manchester and The University of Manchester
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Keywords:
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Abstract:
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Longitudinal and survival sub-models are two building blocks for joint modelling of longitudinal and time to event data. Extensive research indicates separate analysis of these two processes could result in biased outputs due to their associations. Conditional independence between measurements of biomarkers and event time given latent classes or shared random effects is a common approach for characterising the association between the two processes while taking the heterogeneity among the population into account. However, this assumption is tricky to validate because of the unobservable latent variables. Thus a Gaussian copula joint model with random effects is proposed to accommodate the scenarios where the conditional independence assumption is questionable. In our proposed model, the conventional joint model assuming conditional independence is a special case when the association parameter in the Gaussian copula shrinks to zero. Simulation studies and real data application are carried out to evaluate the performance of our proposed model. In addition, personalised dynamic predictions of survival probabilities are obtained based on the proposed model and comparisons are made to the predictions obtained under the conventional joint model.
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Authors who are presenting talks have a * after their name.
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