Hu Jinxiang, Clark Lauren, Shi Peng, Staggs Vincent S, Daley Christine, Gajewski Byron
Department of Biostatistics & Data Science, University of Kansas Medical Center.
Biostatistics & Epidemiology Core, Health Services & Outcomes Research, Children's Mercy Kansas City, and University of Missouri-Kansas City School of Medicine.
Rev Colomb Estad. 2021 Jul;44(2):313-329. Epub 2021 Jul 12.
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.
患者报告结局作为临床结局的重要指标，在以患者为中心的健康结局研究和生活质量研究中受到越来越多的关注，尤其是对于慢性病患者。因子分析是测量患者报告结局的理想方法。如果患者群体存在异质性且样本量较小时，项目功能差异和收敛问题是应用因子模型的挑战。贝叶斯分层因子分析可以通过评估项目功能差异来评估健康差异，同时避免收敛问题。我们进行了一项模拟研究，并以美国印第安少数族裔为例进行实证分析，结果表明，无论人群异质性和样本量如何，拟合贝叶斯分层因子模型都是一个最优解决方案。
