Joint Bayesian longitudinal models for mixed outcome types and associated model selection techniques

Nicholas Seedorff; Grant Brown; Breanna Scorza; Christine A. Petersen

doi:10.1007/s00180-022-01280-x

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Joint Bayesian longitudinal models for mixed outcome types and associated model selection techniques

Journal article

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Peer reviewed

Joint Bayesian longitudinal models for mixed outcome types and associated model selection techniques

Nicholas Seedorff, Grant Brown, Breanna Scorza and Christine A. Petersen

Computational statistics, Vol.38(4), pp.1735-1769

12/2023

DOI: 10.1007/s00180-022-01280-x

PMCID: PMC10825672

PMID: 38292019

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https://pmc.ncbi.nlm.nih.gov/articles/PMC10825672/pdf/nihms-1893623.pdfView

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Abstract

Motivated by data measuring progression of leishmaniosis in a cohort of US dogs, we develop a Bayesian longitudinal model with autoregressive errors to jointly analyze ordinal and continuous outcomes. Multivariate methods can borrow strength across responses and may produce improved longitudinal forecasts of disease progression over univariate methods. We explore the performance of our proposed model under simulation, and demonstrate that it has improved prediction accuracy over traditional Bayesian hierarchical models. We further identify an appropriate model selection criterion. We show that our method holds promise for use in the clinical setting, particularly when ordinal outcomes are measured alongside other variables types that may aid clinical decision making. This approach is particularly applicable when multiple, imperfect measures of disease progression are available.

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Statistics & Probability

Details

Title: Subtitle: Joint Bayesian longitudinal models for mixed outcome types and associated model selection techniques
Creators: Nicholas Seedorff - University of Iowa
Grant Brown - Univ Iowa, Dept Biostat, Coll Publ Hlth, Iowa City, IA 52242 USA
Breanna Scorza - Univ Iowa, Dept Epidemiol, Coll Publ Hlth, Iowa City, IA USA
Christine A. Petersen - Univ Iowa, Dept Epidemiol, Coll Publ Hlth, Iowa City, IA USA
Resource Type: Journal article
Publication Details: Computational statistics, Vol.38(4), pp.1735-1769
DOI: 10.1007/s00180-022-01280-x
PMID: 38292019
PMCID: PMC10825672
NLM abbreviation: Comput Stat
ISSN: 0943-4062
eISSN: 1613-9658
Publisher: Springer Nature
Number of pages: 35
Grant note: R01AI139267 / National Institute of Allergy and Infectious Disease of the National National Institutes of Health
Language: English
Electronic publication date: 09/18/2022
Date published: 12/2023
Academic Unit: Epidemiology; Biostatistics; Internal Medicine
Record Identifier: 9984306253202771

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