Bayesian Hierarchical Factor Analysis for Efficient Estimation across Race/Ethnicity.

Creator(s)

Jinxiang Hu
Lauren Clark
Peng Shi
Vincent S. Staggs, Children's Mercy HospitalFollow
Christine Daley
Byron Gajewski

Document Type

Article

Publication Date

7-2021

Identifier

DOI: 10.15446/rce.v44n2.87690

Abstract

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.

Journal Title

Rev Colomb Estad

Volume

44

Issue

2

First Page

313

Last Page

329

Keywords

American Indians; Bayesian hierarchical model; differential item functioning; factor analysis; health disparities; patient reported outcomes

Comments

Grant support

• P20 MD004805/MD/NIMHD NIH HHS/United States
• P30 CA168524/CA/NCI NIH HHS/United States
• UL1 TR002366/TR/NCATS NIH HHS/United States

This work is licensed under a Creative Commons Attribution 4.0 International License.

Publisher's Link: https://revistas.unal.edu.co/index.php/estad/article/view/87690

Recommended Citation

Hu J, Clark L, Shi P, Staggs VS, Daley C, Gajewski B. Bayesian Hierarchical Factor Analysis for Efficient Estimation across Race/Ethnicity. Rev Colomb Estad. 2021;44(2):313-329.