Assessing individual and population variability in degenerative joint disease prevalence using generalized linear mixed models

Alonso-Llamazares, Carmen, Blanco Márquez, Beatriz, Lopez, Belen and Pardinas, Antonio F. ORCID: https://orcid.org/0000-0001-6845-7590 2021. Assessing individual and population variability in degenerative joint disease prevalence using generalized linear mixed models. American Journal of Physical Anthropology 175 (3) , pp. 611-625. 10.1002/ajpa.24195

Preview

PDF - Accepted Post-Print Version Download (728kB) | Preview

Abstract

Objectives In this paper, we introduce the use of generalized linear mixed models (GLMM) as a better alternative to traditional statistical methods for studying factors associated to the prevalence of degenerative joint disease (DJD) in bioarchaeological contexts. Materials and Methods DJD prevalence was assessed for the appendicular joints and the spine of a Spanish population dated from the 15th to the 18th century. Data were analyzed using contingency tables, logistic regression models, and logistic GLMM. Results In general, results from GLMMs find agreement in other methods. However, by being able to analyze the data at the level of individual bones instead of aggregated joints or limbs, GLMMs are capable of revealing associations that are not evident in other frameworks. Discussion Currently widely available in statistical analysis software, GLMMs can accommodate a wide array of data distributions, account for hierarchical correlations, and return estimates of DJD prevalence within individuals and skeletal locations that are unbiased by the effect of covariates. This gives clear advantages for the analysis of bioarchaeological datasets which can lead to more robust and comparable analyses across populations.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG)
Publisher:	Wiley
ISSN:	0002-9483
Date of First Compliant Deposit:	4 January 2021
Date of Acceptance:	19 November 2020
Last Modified:	07 Nov 2023 07:04
URI:	https://orca.cardiff.ac.uk/id/eprint/137256

Citation Data

Cited 1 time in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item