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Lookup NU author(s): Dr Pete PhilipsonORCiD
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© 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.
Author(s): Philipson P, Hickey GL, Crowther MJ, Kolamunnage-Dona R
Publication type: Article
Publication status: Published
Journal: Computational Statistics and Data Analysis
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
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