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Faster Monte Carlo estimation of joint models for time-to-event and multivariate longitudinal data

Lookup NU author(s): Dr Pete PhilipsonORCiD

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Abstract

© 2020. Quasi-Monte Carlo (QMC) methods using quasi-random sequences, as opposed to pseudo-random samples, are proposed for use in the joint modelling of time-to-event and multivariate longitudinal data. The QMC integration framework extends the Monte Carlo Expectation Maximisation approaches that are commonly adopted, namely using ordinary and antithetic variates. The motivation of QMC integration is to increase the convergence speed by using nodes that are scattered more uniformly. Through simulation, estimates and computational times are compared and this is followed with an application to a clinical dataset. There is a distinct speed advantage in using QMC methods for small sample sizes and QMC is comparable to the antithetic MC method for moderate sample sizes. The new method is available in an updated version of the R package joineRML.

Publication metadata

Author(s): Philipson P, Hickey GL, Crowther MJ, Kolamunnage-Dona R

Publication type: Article

Publication status: Published

Journal: Computational Statistics and Data Analysis

Year: 2020

Volume: 151

Print publication date: 01/11/2020

Online publication date: 27/05/2020

Acceptance date: 06/05/2020

ISSN (print): 0167-9473

ISSN (electronic): 1872-7352

Publisher: Elsevier BV

URL: https://doi.org/10.1016/j.csda.2020.107010

DOI: 10.1016/j.csda.2020.107010

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