A fast approximate EM algorithm for joint models of survival and multivariate longitudinal data

James Murray and Pete Philipson

Computational Statistics & Data Analysis, 2022, vol. 170, issue C

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.

Keywords: Joint models; EM algorithm; Multivariate normal; Simulation; Multiple longitudinal (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:170:y:2022:i:c:s0167947322000184

DOI: 10.1016/j.csda.2022.107438

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