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首页> 外文期刊>Quality of Life Research >Multidimensional Computerized Adaptive Testing of the EORTC QLQ-C30: Basic Developments and Evaluations
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Multidimensional Computerized Adaptive Testing of the EORTC QLQ-C30: Basic Developments and Evaluations

机译：EORTC QLQ-C30的多维计算机自适应测试：基本发展和评估

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摘要

Objective: Self-report questionnaires are widely used to measure health-related quality of life (HRQOL). Ideally, such questionnaires should be adapted to the individual patient and at the same time scores should be directly comparable across patients. This may be achieved using computerized adaptive testing (CAT). Usually, CAT is carried out for a single domain at a time. However, many HRQOL domains are highly correlated. Multidimensional CAT may utilize these correlations to improve measurement efficiency. We investigated the possible advantages and difficulties of multidimensional CAT. Study design and setting: We evaluated multidimensional CAT of three scales from the EORTC QLQ-C30: the physical functioning, emotional functioning, and fatigue scales. Analyses utilised a database with 2958 European cancer patients.Results: It was possible to obtain scores for the three domains with five to seven items administered using multidimensional CAT that were very close to the scores obtained using all 12 items and with no or little loss of measurement precision.Conclusion: The findings suggest that multidimensional CAT may significantly improve measurement precision and efficiency and encourage further research into multidimensional CAT. Particularly, the estimation of the model underlying the multidimensional CAT and the conceptual aspects need further investigations.
机译：目的：自我报告调查表被广泛用于衡量健康相关生活质量（HRQOL）。理想情况下，此类问卷应适合于各个患者，同时分数应在各个患者之间具有可比性。这可以使用计算机自适应测试（CAT）来实现。通常，一次只对单个域执行一次CAT。但是，许多HRQOL域是高度相关的。多维CAT可以利用这些相关性来提高测量效率。我们研究了多维CAT的可能优点和困难。研究设计和设置：我们从EORTC QLQ-C30评估了三个量表的多维CAT：身体功能量，情绪功能量和疲劳量表。分析使用了一个包含2958名欧洲癌症患者的数据库。结果：可以通过使用多维CAT进行5到7个项目获得这三个域的得分，这些得分非常接近使用所有12个项目获得的得分，并且没有或几乎没有损失结论：研究结果表明多维CAT可以显着提高测量精度和效率，并鼓励进一步研究多维CAT。特别是，多维CAT和概念方面的模型估计需要进一步研究。

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