Spatiotemporal signal detection using continuous shrinkage priors

An-Ting Jhuang; Montserrat Fuentes; Dipankar Bandyopadhyay; Brian J Reich

doi:10.1002/sim.8514

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Spatiotemporal signal detection using continuous shrinkage priors

Journal article

Open access

Peer reviewed

Spatiotemporal signal detection using continuous shrinkage priors

An-Ting Jhuang, Montserrat Fuentes, Dipankar Bandyopadhyay and Brian J Reich

Statistics in medicine, Vol.39(13), pp.1817-1832

02/27/2020

DOI: 10.1002/sim.8514

PMCID: PMC7561003

PMID: 32106341

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https://www.ncbi.nlm.nih.gov/pmc/articles/7561003View

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Abstract

Periodontal disease (PD) is a chronic inflammatory disease that affects the gum tissue and bone supporting the teeth. Although tooth-site level PD progression is believed to be spatio-temporally referenced, the whole-mouth average periodontal pocket depth (PPD) has been commonly used as an indicator of the current/active status of PD. This leads to imminent loss of information, and imprecise parameter estimates. Despite availability of statistical methods that accommodates spatiotemporal information for responses collected at the tooth-site level, the enormity of longitudinal databases derived from oral health practice-based settings render them unscalable for application. To mitigate this, we introduce a Bayesian spatiotemporal model to detect problematic/diseased tooth-sites dynamically inside the mouth for any subject obtained from large databases. This is achieved via a spatial continuous sparsity-inducing shrinkage prior on spatially varying linear-trend regression coefficients. A low-rank representation captures the nonstationary covariance structure of the PPD outcomes, and facilitates the relevant Markov chain Monte Carlo computing steps applicable to thousands of study subjects. Application of our method to both simulated data and to a rich database of electronic dental records from the HealthPartners Institute reveal improved prediction performances, compared with alternative models with usual Gaussian priors for regression parameters and conditionally autoregressive specification of the covariance structure.

shrinkage priors

nonstationary covariance

periodontal disease

space-time disease surveillance

Details

Title: Subtitle: Spatiotemporal signal detection using continuous shrinkage priors
Creators: An-Ting Jhuang - Principal Data Scientist, UnitedHealth Group Research & Development, Minnetonka, Minnesota
Montserrat Fuentes - Department of Statistics and Acturial Science & Provost, University of Iowa, Iowa City, Iowa
Dipankar Bandyopadhyay - Department of Biostatistics, Virginia Commonwealth University, Richmond, Virginia
Brian J Reich - Department of Statistics, North Carolina State University, Raleigh, North Carolina
Resource Type: Journal article
Publication Details: Statistics in medicine, Vol.39(13), pp.1817-1832
DOI: 10.1002/sim.8514
PMID: 32106341
PMCID: PMC7561003
NLM abbreviation: Stat Med
ISSN: 0277-6715
eISSN: 1097-0258
Publisher: England
Grant note: R01-DE024984-01A1 / Foundation for the National Institutes of Health
Language: English
Date published: 02/27/2020
Academic Unit: Statistics and Actuarial Science; President; Biostatistics
Record Identifier: 9984065880402771

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