Published

2021-07-12

Bayesian Hierarchical Factor Analysis for Efficient Estimation Across Race/Ethnicity

Estimación eficiente a través de raza y etnicidad usando análisis factorial jerárquico bayesiano

DOI:

https://doi.org/10.15446/rce.v44n2.87690

Keywords:

patient reported outcomes, factor analysis, differential item functioning, Bayesian hierarchical factor analysis, health disparity, American Indian (en)
Análisis factorial, Disparidades en salud, Funcionamiento diferencial de ítems, Indígena americano, Modelo jerárquico Bayesiano, Respuestas reportadas por el paciente (es)

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Authors

  • Jinxiang Hu Department of Biostatistics & Data Science, University of Kansas Medical Center
  • Lauren Clark Department of Biostatistics & Data Science, University of Kansas Medical Center
  • Peng Shi Department of Biostatistics & Data Science, University of Kansas Medical Center
  • Vincent Staggs Biostatistics & Epidemiology Core, Health Services & Outcomes Research, Children’s Mercy Kansas City, and University of Missouri-Kansas City School of Medicine
  • Christine Daley Department of Family Medicine, University of Kansas Medical Center
  • Byron Gajewski Department of Biostatistics & Data Science, University of Kansas Medical Center

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 di˙erential item functioning, while avoiding convergence problems. We conducted a simulation study and used an empirical example with American Indian minorities to show thatffitting a Bayesian hierarchical factor model is an optimal solution regardless of heterogeneity of population and sample size.

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.

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How to Cite

APA

Hu, J., Clark, L., Shi, P., Staggs, V., Daley, C. and Gajewski, B. (2021). Bayesian Hierarchical Factor Analysis for Efficient Estimation Across Race/Ethnicity. Revista Colombiana de Estadística, 44(2), 313–329. https://doi.org/10.15446/rce.v44n2.87690

ACM

[1]
Hu, J., Clark, L., Shi, P., Staggs, V., Daley, C. and Gajewski, B. 2021. Bayesian Hierarchical Factor Analysis for Efficient Estimation Across Race/Ethnicity. Revista Colombiana de Estadística. 44, 2 (Jul. 2021), 313–329. DOI:https://doi.org/10.15446/rce.v44n2.87690.

ACS

(1)
Hu, J.; Clark, L.; Shi, P.; Staggs, V.; Daley, C.; Gajewski, B. Bayesian Hierarchical Factor Analysis for Efficient Estimation Across Race/Ethnicity. Rev. colomb. estad. 2021, 44, 313-329.

ABNT

HU, J.; CLARK, L.; SHI, P.; STAGGS, V.; DALEY, C.; GAJEWSKI, B. Bayesian Hierarchical Factor Analysis for Efficient Estimation Across Race/Ethnicity. Revista Colombiana de Estadística, [S. l.], v. 44, n. 2, p. 313–329, 2021. DOI: 10.15446/rce.v44n2.87690. Disponível em: https://revistas.unal.edu.co/index.php/estad/article/view/87690. Acesso em: 4 aug. 2024.

Chicago

Hu, Jinxiang, Lauren Clark, Peng Shi, Vincent Staggs, Christine Daley, and Byron Gajewski. 2021. “Bayesian Hierarchical Factor Analysis for Efficient Estimation Across Race/Ethnicity”. Revista Colombiana De Estadística 44 (2):313-29. https://doi.org/10.15446/rce.v44n2.87690.

Harvard

Hu, J., Clark, L., Shi, P., Staggs, V., Daley, C. and Gajewski, B. (2021) “Bayesian Hierarchical Factor Analysis for Efficient Estimation Across Race/Ethnicity”, Revista Colombiana de Estadística, 44(2), pp. 313–329. doi: 10.15446/rce.v44n2.87690.

IEEE

[1]
J. Hu, L. Clark, P. Shi, V. Staggs, C. Daley, and B. Gajewski, “Bayesian Hierarchical Factor Analysis for Efficient Estimation Across Race/Ethnicity”, Rev. colomb. estad., vol. 44, no. 2, pp. 313–329, Jul. 2021.

MLA

Hu, J., L. Clark, P. Shi, V. Staggs, C. Daley, and B. Gajewski. “Bayesian Hierarchical Factor Analysis for Efficient Estimation Across Race/Ethnicity”. Revista Colombiana de Estadística, vol. 44, no. 2, July 2021, pp. 313-29, doi:10.15446/rce.v44n2.87690.

Turabian

Hu, Jinxiang, Lauren Clark, Peng Shi, Vincent Staggs, Christine Daley, and Byron Gajewski. “Bayesian Hierarchical Factor Analysis for Efficient Estimation Across Race/Ethnicity”. Revista Colombiana de Estadística 44, no. 2 (July 12, 2021): 313–329. Accessed August 4, 2024. https://revistas.unal.edu.co/index.php/estad/article/view/87690.

Vancouver

1.
Hu J, Clark L, Shi P, Staggs V, Daley C, Gajewski B. Bayesian Hierarchical Factor Analysis for Efficient Estimation Across Race/Ethnicity. Rev. colomb. estad. [Internet]. 2021 Jul. 12 [cited 2024 Aug. 4];44(2):313-29. Available from: https://revistas.unal.edu.co/index.php/estad/article/view/87690

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