School of Nursing, Trinity Western University, 7600 Glover Rd, Langley, BC, V2Y1Y1, Canada.
Centre for Health Evaluation and Outcome Sciences, Providence Health Care, Vancouver, BC, Canada.
Qual Life Res. 2018 Jul;27(7):1745-1755. doi: 10.1007/s11136-017-1680-8. Epub 2017 Aug 23.
Patient-reported outcome measures (PROMs) are frequently used in heterogeneous patient populations. PROM scores may lead to biased inferences when sources of heterogeneity (e.g., gender, ethnicity, and social factors) are ignored. Latent variable mixture models (LVMMs) can be used to examine measurement invariance (MI) when sources of heterogeneity in the population are not known a priori. The goal of this article is to discuss the use of LVMMs to identify invariant items within the context of test construction.
The Draper-Lindely-de Finetti (DLD) framework for the measurement of latent variables provides a theoretical context for the use of LVMMs to identify the most invariant items in test construction. In an expository analysis using 39 items measuring daily activities, LVMMs were conducted to compare 1- and 2-class item response theory models (IRT). If the 2-class model had better fit, item-level logistic regression differential item functioning (DIF) analyses were conducted to identify items that were not invariant. These items were removed and LVMMs and DIF testing repeated until all remaining items showed MI.
The 39 items had an essentially unidimensional measurement structure. However, a 1-class IRT model resulted in many statistically significant bivariate residuals, indicating suboptimal fit due to remaining local dependence. A 2-class LVMM had better fit. Through subsequent rounds of LVMMs and DIF testing, nine items were identified as being most invariant.
The DLD framework and the use of LVMMs have significant potential for advancing theoretical developments and research on item selection and the development of PROMs for heterogeneous populations.
患者报告结局测量（PROM）常用于异质患者群体。如果忽略异质源（例如，性别、种族和社会因素），PROM 评分可能会导致有偏差的推断。潜在变量混合模型（LVMM）可用于在人群中异质源未知的情况下检查测量不变性（MI）。本文的目的是讨论在测试构建中使用 LVMM 识别不变项目。
Draper-Lindely-de Finetti（DLD）框架用于测量潜在变量，为使用 LVMM 识别测试构建中最不变的项目提供了理论背景。在使用 39 个测量日常活动的项目的说明性分析中，进行了 LVMM 以比较 1 类和 2 类项目反应理论模型（IRT）。如果 2 类模型拟合更好，则进行项目级逻辑回归差异项目功能（DIF）分析以识别不变的项目。删除这些项目，并重复 LVMM 和 DIF 测试，直到所有剩余项目都显示 MI。
39 个项目具有基本的单维测量结构。然而，1 类 IRT 模型导致许多统计学上显著的双变量残差，表明由于剩余的局部依赖性，拟合不理想。2 类 LVMM 拟合更好。通过随后几轮的 LVMM 和 DIF 测试，确定了 9 个最不变的项目。
DLD 框架和 LVMM 的使用对推进异质人群的项目选择和 PROM 开发的理论发展和研究具有重要潜力。
