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

被引:15
|
作者
Philipson, Pete [1 ]
Hickey, Graeme L. [2 ]
Crowther, Michael J. [3 ]
Kolamunnage-Dona, Ruwanthi [2 ]
机构
[1] Newcastle Univ, Sch Math Stat & Phys, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England
[2] Univ Liverpool, Inst Translat Med, Dept Biostat, Liverpool, Merseyside, England
[3] Univ Leicester, Dept Hlth Sci, Biostat Res Grp, Leicester, Leics, England
基金
英国医学研究理事会;
关键词
Quasi Monte Carlo; Joint modelling; Multivariate longitudinal; Time-to-event; EM algorithms; MAXIMUM-LIKELIHOOD; SURVIVAL-DATA; SOBOL;
D O I
10.1016/j.csda.2020.107010
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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. Crown Copyright (C) 2020 Published by Elsevier B.V. All rights reserved.
引用
收藏
页数:14
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