Assessing and accounting for between-sample variation of psychometric measurement models

目前的患者报告结果(PRO)心理测量学模型通常假设模型参数不是随机的，而是固定的。在PRO分析中，这些模型被用于独立的样本，如差异项目功能、因素分析或基于结构的量表的开发，人们经常观察到，模型参数通常在不同样本之间存在差异。由于这些差异在统计上是显著的，但实际上是无关的，因此有希望将它们明确地建模为随机效应，以改进PRO测量和相关推理。本项目的目标是通过56项不同的研究，17.357名参与者，在PHQ-9的个人参与者数据荟萃分析中调查这一现象。将评估模型参数的样本间方差，并将使用贝叶斯模型将这些信息纳入分析。更具体地说，该项目的目的是(1)调查验证性因素分析和项目反应理论中模型参数估计的样本间变异性，(2)确定研究水平上的变量，如样本量、原籍国、疾病组、年龄和性别，这些变量与模型参数的系统和相关变化有关，(3)在包含样本间随机参数变化信息的贝叶斯项目反应理论模型中估计抑郁严重程度，以及(4)通过比较由这些模型得出的抑郁严重程度估计的诊断准确率与常用的PHQ-9总分，来研究这种模型对预测有效性的影响。本项目将为经常使用且高度相关的专业心理测量模型的样本间系统和非系统变化的本质提供洞察。此外，它还将提供一种手段，说明专业技能统计分析中微小的、实际上不相关的模型差异，从而为今后测量模型的发展提供参考，例如，在制定专业技能技能的独立于仪器的、基于结构的量表方面。

Current psychometric models of patient-reported outcomes (PROs) typically assume that model parameters are not random but fixed. In analyses of PROs, where such models are used in independent samples such as differential item functioning, factor analysis or the development of construct-based scales, it has been frequently observed that model parameters usually vary across samples. As these differences are often statistically significant, but practically irrelevant, it is promising to explicitly model them as random effects in order to improve PRO measurement and related inference.The objective of this project is to investigate this phenomena across 56 distinct studies with 17.357 participants, collected within an individual participant data meta-analysis of the PHQ-9. Between-sample variance of model parameters will be assessed and Bayesian models will be used to incorporate such information in analysis. More specifically, the aims of the project are (1) to investigate between-samples variability of model parameter estimates both in confirmatory factor analyis and item-response theory, (2) to identify variables on study level such as sample size, country of origin, disease groups, age and gender, which are associated with systematic and relevant variation of model parameters, (3) to estimate depression severity within a Bayesian item-response theory model which incorporates information about random parameter variation across samples and to (4) to investigate impact of such a model on predictive validity by comparing the diagnostic accuracy of estimates of depression severity derived by these models with PHQ-9 sum scores commonly used.This project will provide insights into the nature of systematic and unsystematic between-sample variation of psychometric models for a frequently used and highly relevant PRO, the PHQ-9. Furthermore, it will provide means to account for minor, practically irrelevant model differences in statistical analysis of PROs and therefore inform development of future measurement models, e.g. in the development of instrument-independent, construct-based scales for PROs.