ArticleStatistics in medicine2013
An assessment of estimation methods for generalized linear mixed models with binary outcomes.
Article in Statistics in medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis of binary outcomes via generalized linear mixed models: a simulation study.BMC medical research methodology · 2018Pooled it
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- Design of Bayesian Clinical Trials With Clustered Data.Statistics in medicine · 2026Article
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- The Neurobiology of Cognitive Fatigue and Its Influence on Effort-Based Choice.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Variation in Reporting of Incidental Findings on Initial Lung Cancer Screening and Associations With Clinician Assessment.Journal of the American College of Radiology : JACR · 2024Article
- The role of therapy delivery and clinic organizational factors in explaining therapist effects for trauma-focused psychotherapies in the Veterans Health Administration.Journal of consulting and clinical psychology · 2023Article
- A Public Health Critical Race Praxis Informed Congestive Heart Failure Quality Improvement Initiative on Inpatient General Medicine.Journal of general internal medicine · 2023Article
- A Comparison of Mathematical and Statistical Modeling with Longitudinal Data: An Application to Ecological Momentary Assessment of Behavior Change in Individuals with Alcohol Use Disorder.Bulletin of mathematical biology · 2022Article
- Receipt of evidence-based alcohol-related care in a national sample of transgender patients with unhealthy alcohol use: Overall and relative to non-transgender patients.Journal of substance abuse treatment · 2021Article
- High performance implementation of the hierarchical likelihood for generalized linear mixed models: an application to estimate the potassium reference range in massive electronic health records datasets.BMC medical research methodology · 2021Article
- Observational stepped-wedge analysis of a community health worker-led intervention for diabetes and hypertension in rural Mexico.BMJ open · 2020Observational
- Racial discrimination and the health and wellbeing of Aboriginal and Torres Strait Islander children: Does the timing of first exposure matter?SSM - population health · 2019Article
- Caregiver-perceived racial discrimination is associated with diverse mental health outcomes in Aboriginal and Torres Strait Islander children aged 7-12 years.International journal for equity in health · 2019Article
- Using mixed effects logistic regression models for complex survey data on malaria rapid diagnostic test results.Malaria journal · 2018Article
- A measure of association for ordered categorical data in population-based studies.Statistical methods in medical research · 2018Article
- Participation and performance on paper- and computer-based low-stakes assessments.International journal of STEM education · 2018Article
- Bayesian models for semicontinuous outcomes in rolling admission therapy groups.Psychological methods · 2017Article
Corrections and comments
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3 authors.
Funding
Abstract
Two main classes of methodology have been developed for addressing the analytical intractability of generalized linear mixed models: likelihood-based methods and Bayesian methods. Likelihood-based methods such as the penalized quasi-likelihood approach have been shown to produce biased estimates especially for binary clustered data with small clusters sizes. More recent methods using adaptive Gaussian quadrature perform well but can be overwhelmed by problems with large numbers of random effects, and efficient algorithms to better handle these situations have not yet been integrated in standard statistical packages. Bayesian methods, although they have good frequentist properties when the model is correct, are known to be computationally intensive and also require specialized code, limiting their use in practice. In this article, we introduce a modification of the hybrid approach of Capanu and Begg, 2011, Biometrics 67, 371-380, as a bridge between the likelihood-based and Bayesian approaches by employing Bayesian estimation for the variance components followed by Laplacian estimation for the regression coefficients. We investigate its performance as well as that of several likelihood-based methods in the setting of generalized linear mixed models with binary outcomes. We apply the methods to three datasets and conduct simulations to illustrate their properties. Simulation results indicate that for moderate to large numbers of observations per random effect, adaptive Gaussian quadrature and the Laplacian approximation are very accurate, with adaptive Gaussian quadrature preferable as the number of observations per random effect increases. The hybrid approach is overall similar to the Laplace method, and it can be superior for data with very sparse random effects.
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