Amsterdam UMC, Vrije Universiteit Amsterdam, Ophthalmology, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
Bergman Clinics, Department of Ophthalmology, The Netherlands.
Transl Vis Sci Technol. 2022 Apr 1;11(4):5. doi: 10.1167/tvst.11.4.5.
This study aims to develop an item-bank to measure vision-related quality of life (Vr-QoL) and subsequently calibrate this set of items.
Three Vr-QoL instruments were searched for suitable items to be added in the EyeQ. Patients who received antivascular endothelial growth factor treatment for various retinal diseases involving macular edema were included in the study and completed the 47-item EyeQ. Item response theory (IRT) was used to calibrate the EyeQ items, which was performed multiple times in subsets as a novel approach, containing 80% of the data. Differential item functioning (DIF) was evaluated for various variables.
Responses of 704 patients were used in analysis. One item violated the local independence IRT-assumption and showed a high percentage of missing values, after which this item was deleted from the item-bank. The data of the five subsets fitted the graded response model adequately, and no DIF was detected for items between subsets, after which mean item parameters were calculated. Item fit statistics were found to be good. DIF was detected for gender, age, and administration mode by the patient (independently vs. with help), this involved three items, which all showed negligible impact on total scores.
Because of separate calibrations of the EyeQ in multiple subsets, a high robustness of item parameters is expected.
The calibrated EyeQ can now be used for the assessment of Vr-QoL in patients suffering from exudative retinal diseases and is promising for use as a computer adaptive test.
本研究旨在开发一个用于测量与视觉相关的生活质量(Vr-QoL)的项目库,并对该套项目进行校准。
搜索了三种 Vr-QoL 工具,以寻找适合添加到 EyeQ 中的项目。本研究纳入了接受抗血管内皮生长因子治疗各种涉及黄斑水肿的视网膜疾病的患者,并让他们完成包含 47 个项目的 EyeQ。采用项目反应理论(IRT)对 EyeQ 项目进行校准,这是一种新颖的方法,多次在数据子集上进行,包含 80%的数据。评估了各种变量的差异项目功能(DIF)。
共分析了 704 名患者的反应。有一个项目违反了局部独立性 IRT 假设,且有很高的缺失值百分比,因此将其从项目库中删除。五个子集的数据充分拟合了等级反应模型,且各子集之间的项目没有检测到 DIF,之后计算了平均项目参数。项目拟合统计数据良好。在性别、年龄和患者的评估方式(独立评估与辅助评估)方面检测到三个项目的 DIF,这三个项目都对总分的影响可忽略不计。
由于 EyeQ 在多个子集中进行了单独的校准,预计项目参数的稳健性会很高。
原文中没有脚注和尾注,因此我没有添加任何注释。