Resumen

HU, JINXIANG et al. Bayesian Hierarchical Factor Analysis for Eficient Estimation Across Race/Ethnicity. Rev.Colomb.Estad. [online]. 2021, vol.44, n.2, pp.313-329.  Epub 31-Ago-2021. ISSN 0120-1751.  https://doi.org/10.15446/rce.v44n2.87690.

Patient reported outcomes are gaining more attention in patient-centered health outcomes research and quality of life studies as important indicators of clinical outcomes, especially for patients with chronic diseases. Factor analysis is ideal for measuring patient reported outcomes. If there is heterogeneity in the patient population and when sample size is small, differential item functioning and convergence issues are challenges for applying factor models. Bayesian hierarchical factor analysis can assess health disparity by assessing for differential item functioning, while avoiding convergence problems. We conducted a simulation study and used an empirical example with American Indian minorities to show that fitting a Bayesian hierarchical factor model is an optimal solution regardless of heterogeneity of population and sample size.

Palabras clave : American Indians; Bayesian hierarchical model; Di_erential item functioning; Factor analysis; Health disparities; Patient reported outcomes.

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