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A Fast Approximate EM Algorithm for Joint Models of Survival and Multivariate Longitudinal Data

Lookup NU author(s): Dr James Murray, Dr Pete PhilipsonORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

Joint models are an increasingly popular way to characterise the relationship between one or more longitudinal responses and an event of interest. However, for multivariate joint models the increased dimensionality and complexity of random effects present in the model specification are commensurate with increased computing time, hampering the implementation of many classic approaches. An approximate EM algorithm which ameliorates the so-called ‘curse of dimensionality’ is developed. The scaleability and accuracy of the proposed method are demonstrated via two simulation studies and applied to data arising from two clinical trials in the disease areas of cirrhosis and Alzheimer's disease, each with three biomarkers.


Publication metadata

Author(s): Murray J, Philipson P

Publication type: Article

Publication status: Published

Journal: Computational Statistics and Data Analysis

Year: 2022

Volume: 170

Print publication date: 01/06/2022

Online publication date: 15/02/2022

Acceptance date: 21/01/2022

Date deposited: 15/02/2022

ISSN (electronic): 0167-947

Publisher: Elsevier

URL: https://doi.org/10.1016/j.csda.2022.107438

DOI: 10.1016/j.csda.2022.107438


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Funding

Funder referenceFunder name
EP/V520184/1
EPSRC

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