A Bayesian hierarchical logistic regression model of multiple informant family health histories.


National Institutes of Health, Bethesda, MD, USA. [Email]


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.


Bayesian statistics,Family health history,Multiple informants,Reconciliation,

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