Dubuy Yseulys, Hardouin Jean-Benoit, Blanchin Myriam, Sébille Véronique
UMR INSERM 1246, MethodS in Patients-centered outcomes and HEalth ResEarch (SPHERE), Nantes Université, Nantes, France.
Methodology and Biostatistics Unit, CHU Nantes, Nantes Université, Nantes, France.
Front Psychol. 2023 Aug 10;14:1191107. doi: 10.3389/fpsyg.2023.1191107. eCollection 2023.
When analyzing patient-reported outcome (PRO) data, sources of differential item functioning (DIF) can be multiple and there may be more than one covariate of interest. Hence, it could be of great interest to disentangle their effects. Yet, in the literature on PRO measures, there are many studies where DIF detection is applied separately and independently for each covariate under examination. With such an approach, the covariates under investigation are not introduced together in the analysis, preventing from simultaneously studying their potential DIF effects on the questionnaire items. One issue, among others, is that it may lead to the detection of false-positive effects when covariates are correlated. To overcome this issue, we developed two new algorithms (namely ROSALI-DIF FORWARD and ROSALI-DIF BACKWARD). Our aim was to obtain an iterative item-by-item DIF detection method based on Rasch family models that enable to adjust group comparisons for DIF in presence of two binary covariates. Both algorithms were evaluated through a simulation study under various conditions aiming to be representative of health research contexts. The performance of the algorithms was assessed using: (i) the rates of false and correct detection of DIF, (ii) the DIF size and form recovery, and (iii) the bias in the latent variable level estimation. We compared the performance of the ROSALI-DIF algorithms to the one of another approach based on likelihood penalization. For both algorithms, the rate of false detection of DIF was close to 5%. The DIF size and form influenced the rates of correct detection of DIF. Rates of correct detection was higher with increasing DIF size. Besides, the algorithm fairly identified homogeneous differences in the item threshold parameters, but had more difficulties identifying non-homogeneous differences. Over all, the ROSALI-DIF algorithms performed better than the penalized likelihood approach. Integrating several covariates during the DIF detection process may allow a better assessment and understanding of DIF. This study provides valuable insights regarding the performance of different approaches that could be undertaken to fulfill this aim.
在分析患者报告结局（PRO）数据时，差异项目功能（DIF）的来源可能多种多样，并且可能存在多个感兴趣的协变量。因此，厘清它们的影响可能会非常有意义。然而，在关于PRO测量的文献中，有许多研究是针对每个被检查的协变量分别独立地应用DIF检测。采用这种方法时，所研究的协变量在分析中不是一起引入的，从而无法同时研究它们对问卷项目潜在的DIF效应。其中一个问题是，当协变量相关时，这可能会导致检测到假阳性效应。为克服这个问题，我们开发了两种新算法（即ROSALI-DIF向前法和ROSALI-DIF向后法）。我们的目标是获得一种基于Rasch族模型的逐项目迭代DIF检测方法，该方法能够在存在两个二元协变量的情况下针对DIF调整组间比较。通过一项模拟研究在各种旨在代表健康研究背景的条件下对这两种算法进行了评估。使用以下指标评估算法的性能：（i）DIF的错误和正确检测率，（ii）DIF大小和形式的恢复情况，以及（iii）潜在变量水平估计中的偏差。我们将ROSALI-DIF算法的性能与另一种基于似然惩罚的方法进行了比较。对于这两种算法，DIF的错误检测率均接近5%。DIF的大小和形式影响了DIF的正确检测率。随着DIF大小的增加，正确检测率更高。此外，该算法能较好地识别项目阈值参数中的同质差异，但在识别非同质差异方面存在更多困难。总体而言，ROSALI-DIF算法的表现优于惩罚似然法。在DIF检测过程中整合多个协变量可能会更好地评估和理解DIF。本研究为实现这一目标可采用的不同方法的性能提供了有价值的见解。
