A Bayesian hierarchical logistic regression model of multiple informant family health histories View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Jielu Lin, Melanie F. Myers, Laura M. Koehly, Christopher Steven Marcum

ABSTRACT

BACKGROUND: Family health history (FHH) inherently involves collecting proxy reports of health statuses of related family members. Traditionally, such information has been collected from a single informant. More recently, research has suggested that a multiple informant approach to collecting FHH results in improved individual risk assessments. Likewise, recent work has emphasized the importance of incorporating health-related behaviors into FHH-based risk calculations. Integrating both multiple accounts of FHH with behavioral information on family members represents a significant methodological challenge as such FHH data is hierarchical in nature and arises from potentially error-prone processes. METHODS: In this paper, we introduce a statistical model that addresses these challenges using informative priors for background variation in disease prevalence and the effect of other, potentially correlated, variables while accounting for the nested structure of these data. Our empirical example is drawn from previously published data on families with a history of diabetes. RESULTS: The results of the comparative model assessment suggest that simply accounting for the structured nature of multiple informant FHH data improves classification accuracy over the baseline and that incorporating family member health-related behavioral information into the model is preferred over alternative specifications. CONCLUSIONS: The proposed modelling framework is a flexible solution to integrate multiple informant FHH for risk prediction purposes. More... »

PAGES

56

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12874-019-0700-5

DOI

http://dx.doi.org/10.1186/s12874-019-0700-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112758707

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30871571


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