ArticleJournal of the American Statistical Association2019
Bayesian Semiparametric Functional Mixed Models for Serially Correlated Functional Data, with Application to Glaucoma Data.
Article in Journal of the American Statistical Association, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 22 citations in OpenAlex.
- Covariance Assisted Multivariate Penalized Additive Regression (CoMPAdRe).Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2025Article
- Functional Bayesian networks for discovering causality from multivariate functional data.Biometrics · 2023Article
- Article
- Ultra-Fast Approximate Inference Using Variational Functional Mixed Models.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023Article
- Artificial Intelligence to Aid Glaucoma Diagnosis and Monitoring: State of the Art and New Directions.Photonics · 2022Article
- Fixed-effects inference and tests of correlation for longitudinal functional data.Statistics in medicine · 2022Article
- Bayesian analysis of longitudinal and multidimensional functional data.Biostatistics (Oxford, England) · 2022Article
- ORDINAL PROBIT FUNCTIONAL OUTCOME REGRESSION WITH APPLICATION TO COMPUTER-USE BEHAVIOR IN RHESUS MONKEYS.The annals of applied statistics · 2022Article
- From multivariate to functional data analysis: fundamentals, recent developments, and emerging areas.Journal of multivariate analysis · 2022Article
- FUNCTION-ON-FUNCTION REGRESSION FOR THE IDENTIFICATION OF EPIGENETIC REGIONS EXHIBITING WINDOWS OF SUSCEPTIBILITY TO ENVIRONMENTAL EXPOSURES.The annals of applied statistics · 2021Article
- Quantile Function on Scalar Regression Analysis for Distributional Data.Journal of the American Statistical Association · 2020Article
- Comparison and Contrast of Two General Functional Regression Modeling Frameworks.Statistical modelling · 2017Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
Glaucoma, a leading cause of blindness, is characterized by optic nerve damage related to intraocular pressure (IOP), but its full etiology is unknown. Researchers at UAB have devised a custom device to measure scleral strain continuously around the eye under fixed levels of IOP, which here is used to assess how strain varies around the posterior pole, with IOP, and across glaucoma risk factors such as age. The hypothesis is that scleral strain decreases with age, which could alter biomechanics of the optic nerve head and cause damage that could eventually lead to glaucoma. To evaluate this hypothesis, we adapted Bayesian Functional Mixed Models to model these complex data consisting of correlated functions on spherical scleral surface, with nonparametric age effects allowed to vary in magnitude and smoothness across the scleral surface, multi-level random effect functions to capture within-subject correlation, and functional growth curve terms to capture serial correlation across IOPs that can vary around the scleral surface. Our method yields fully Bayesian inference on the scleral surface or any aggregation or transformation thereof, and reveals interesting insights into the biomechanical etiology of glaucoma. The general modeling framework described is very flexible and applicable to many complex, high-dimensional functional data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.