 Faster Monte Carlo estimation of joint models for time-to-event and multivariate longitudinal dataPete Philipson, Graeme L. Hickey, Michael J. Crowther and Ruwanthi Kolamunnage-Dona Computational Statistics & Data Analysis, 2020, vol. 151, issue C Abstract: 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. Keywords: Quasi Monte Carlo; Joint modelling; Multivariate longitudinal; Time-to-event; EM algorithms (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:151:y:2020:i:c:s0167947320301018 DOI: 10.1016/j.csda.2020.107010 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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