Lai Jin-Shei, Dineen Kelly, Reeve Bryce B, Von Roenn Jamie, Shervin Daniel, McGuire Michael, Bode Rita K, Paice Judith, Cella David
Center on Outcomes Research and Education, Evanston Northwestern Healthcare and Northwestern University, Evanston, Illinois 60201, USA.
J Pain Symptom Manage. 2005 Sep;30(3):278-88. doi: 10.1016/j.jpainsymman.2005.03.009.
Cancer-related pain is often under-recognized and undertreated. This is partly due to the lack of appropriate assessments, which need to be comprehensive and precise yet easily integrated into clinics. Computerized adaptive testing (CAT) can enable precise-yet-brief assessments by only selecting the most informative items from a calibrated item bank. The purpose of this study was to create such a bank. The sample included 400 cancer patients who were asked to complete 61 pain-related items. Data were analyzed using factor analysis and the Rasch model. The final bank consisted of 43 items which satisfied the measurement requirement of factor analysis and the Rasch model, demonstrated high internal consistency and reasonable item-total correlations, and discriminated patients with differing degrees of pain. We conclude that this bank demonstrates good psychometric properties, is sensitive to pain reported by patients, and can be used as the foundation for a CAT pain-testing platform for use in clinical practice.
癌症相关疼痛常常未得到充分认识和治疗。部分原因在于缺乏适当的评估,这种评估需要全面、精确且易于融入临床实践。计算机自适应测试(CAT)通过仅从校准过的题库中选择最具信息量的项目,能够实现精确而简洁的评估。本研究的目的是创建这样一个题库。样本包括400名癌症患者,他们被要求完成61个与疼痛相关的项目。使用因子分析和拉施模型对数据进行分析。最终的题库由43个项目组成,这些项目满足因子分析和拉施模型的测量要求,具有较高的内部一致性和合理的项目总分相关性,并且能够区分不同疼痛程度的患者。我们得出结论,这个题库具有良好的心理测量特性,对患者报告的疼痛敏感,可作为临床实践中CAT疼痛测试平台的基础。