Fast fitting of joint models for longitudinal and event time data using a pseudo-adaptive Gaussian quadrature rule

Research output: Contribution to journalArticleAcademicpeer-review

53 Citations (Scopus)

Abstract

Joint models for longitudinal and time-to-event data have recently attracted a lot of attention in statistics and biostatistics. Even though these models enjoy a wide range of applications in many different statistical fields, they have not yet found their rightful place in the toolbox of modern applied statisticians mainly due to the fact that they are rather computationally intensive to fit. The main difficulty arises from the requirement for numerical integration with respect to the random effects. This integration is typically performed using Gaussian quadrature rules whose computational complexity increases exponentially with the dimension of the random-effects vector. A solution to overcome this problem is proposed using a pseudo-adaptive Gauss-Hermite quadrature rule. The idea behind this rule is to use information for the shape of the integrand by separately fitting a mixed model for the longitudinal outcome. Simulation studies show that the pseudo-adaptive rule performs excellently in practice, and is considerably faster than the standard Gauss-Hermite rule. (C) 2011 Elsevier B.V. All rights reserved.
Original languageUndefined/Unknown
Pages (from-to)491-501
Number of pages11
JournalComputational Statistics & Data Analysis
Volume56
Issue number3
DOIs
Publication statusPublished - 2012

Cite this