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Revista Colombiana de Estadística
Print version ISSN 0120-1751
Rev.Colomb.Estad. vol.44 no.2 Bogotá July/Dec. 2021 Epub Aug 31, 2021
https://doi.org/10.15446/rce.v44n2.87690
Original articles of research
Bayesian Hierarchical Factor Analysis for Eficient Estimation Across Race/Ethnicity
Estimación eficiente a través de raza y etnicidad usando análisis factorial jerárquico bayesiano
1 Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, USA
2 Health Services & Outcomes Research, University of Missouri Medical Center, Kansas City, USA
3 Department of Family Medicine, University of Kansas Medical Center, Kansas City, USA
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.
Key words: American Indians; Bayesian hierarchical model; Di_erential item functioning; Factor analysis; Health disparities; Patient reported outcomes
Las repuestas reportadas por el paciente están siendo fuertemente consideradas en la investigación de respuestas de salud centradas en el paciente y en estudios de calidad de vida comos indicadores importantes de respuestas clínicas, especialmente en pacientes con enfermedades crónicas. El análisis factorial es ideal para medir respuestas reportadas por el paciente. Cuando hay heterogeneidad en la población de pacientes y el tamaño muestral es pequeño, diferencias en el funcionamiento de los ítems y problemas de convergencia plantean dificultades para aplicar modelos factoriales. El análisis factorial jerárquico Bayesiano puede evaluar disparidades de salud evaluando el funcionamiento diferencial de los ítems, mientras que evita problemas de convergencia. Hemos realizado un estudio de simulación y empleado un ejemplo empírico con minorías indígenas Americanas para mostrar que el ajuste de un modelo factorial jerárquico Bayesiano es una solución óptima sin importar la heterogeneidad de la población o el tamaño muestral.
Palabras clave: Análisis factorial; Disparidades en salud; Funcionamiento diferencial de ítems; Indígena americano; Modelo jerárquico Bayesiano; Respuestas reportadas por el paciente
Acknowledgements
This work was supported by the University of Kansas Cancer Center (P30 CA168524), by a National Institute on Minority Health and Health Disparities grant (P20MD004805) awarded to Center for American Indian Community Health, and by a Clinical and Translational Science Institute TSA grant from the National Center for Advancing Translational Sciences awarded to the University of Kansas for Frontiers: University of Kansas Clinical and Translational Science Institute (UL1TR002366). We also would like to thank the participants who participated in the Patient Assessment of Mammography Services survey. The contents are solely the responsibility of the authors and do not necessarily represent the oficial views of the NIH or NCATS.
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Received: December 2020; Accepted: March 2021
This is an open-access article distributed under the terms of the Creative Commons Attribution License
