2016 journal article
Bivariate Mixed Effects Analysis of Clustered Data with Large Cluster Sizes
STATISTICS IN BIOSCIENCES, 8(2), 220–233.
author keywords: Meta-analysis; Missing data; Multi-center studies
TL;DR:
An expected and maximization (EM) algorithm for the REML inference and an ML implementation using existing software and the new REML EM algorithm is easy to implement and computationally stable and efficient.
(via Semantic Scholar)
open access via europepmc.org (repository)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
Source: Web Of Science
Added: August 6, 2018
Linear mixed effects models are widely used to analyze a clustered response variable. Motivated by a recent study to examine and compare the hospital length of stay (LOS) between patients undertaking percutaneous coronary intervention (PCI) and coronary artery bypass graft (CABG) from several international clinical trials, we proposed a bivariate linear mixed effects model for the joint modeling of clustered PCI and CABG LOS's where each clinical trial is considered a cluster. Due to the large number of patients in some trials, commonly used commercial statistical software for fitting (bivariate) linear mixed models failed to run since it could not allocate enough memory to invert large dimensional matrices during the optimization process. We consider ways to circumvent the computational problem in the maximum likelihood (ML) inference and restricted maximum likelihood (REML) inference. Particularly, we developed an expected and maximization (EM) algorithm for the REML inference and presented an ML implementation using existing software. The new REML EM algorithm is easy to implement and computationally stable and efficient. With this REML EM algorithm, we could analyze the LOS data and obtained meaningful results.