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HIERARCHICAL BAYESIAN MODELLING OF SOCIAL VARIATION IN THE AGE DEPENDENCE OF DISABILITY PREVALENCE
Author(s) -
Graham Patrick
Publication year - 2005
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/j.1467-842x.2005.00405.x
Subject(s) - bayes' theorem , bayesian hierarchical modeling , bayesian probability , econometrics , variation (astronomy) , multilevel model , hierarchical database model , bayes factor , bayesian inference , contrast (vision) , mathematics , perspective (graphical) , statistics , psychology , demography , computer science , sociology , artificial intelligence , data mining , physics , geometry , astrophysics
Summary Motivated by a study of social variation in the relationship of functional limitation prevalence to age, this paper examines methods for modelling social variation in health outcomes. It is argued that, from a Bayesian perspective, modelling the dependence of functional limitation prevalence on age separately for each social group, corresponds to an implausible prior model, in addition to leading to imprecise estimates for some groups. The alternative strategy of fitting a single model, perhaps including some age‐by‐group interactions but omitting higher‐order interactions, requires a strong prior commitment to the absence of such effects. Hierarchical Bayesian modelling is proposed as a compromise between these two analytical approaches. Under all hierarchical Bayes analyses there is strong evidence for an ethnic group difference in limitation prevalence in early‐ to mid‐adulthood among tertiary‐qualified males. In contrast, the single‐model approach largely misses this effect, while the group‐specific analyses exhibit an unrealistically large degree of heterogeneity in gender‐education‐specific ethnicity effects. The sensitivity of posterior inferences to prior specifications is studied.