Department of Psychology, Neurocognitive Translational Research Lab, Cancer Control & Survivorship Program, University of Notre Dame, 109 Haggar Hall, Notre Dame, IN, 46556, USA,
Support Care Cancer. 2014 Aug;22(8):2229-40. doi: 10.1007/s00520-014-2202-7. Epub 2014 Mar 25.
We developed and validated a Patient Satisfaction with Cancer-Related Care (PSCC) measure using classical test theory methods. The present study applied item response theory (IRT) analysis to determine item-level psychometric properties, facilitate development of short forms, and inform future applications for the PSCC.
We applied unidimensional IRT models to PSCC data from 1,296 participants (73% female; 18 to 86 years). An unconstrained graded response model (GRM) and a Rasch Model were fitted to estimate indices for model comparison using likelihood ratio (LR) test and information criteria. We computed item and latent trait parameter estimates, category and operating characteristic curves, and tested information curves for the better fitting model.
The GRM fitted the data better than the Rasch Model (LR = 828, df = 17, p < 0.001). The log-likelihood (-17,390.38 vs. -17,804.26) was larger, and the AIC and BIC were smaller for the GRM compared to the Rash Model (AIC = 34,960.77 vs. 35,754.73; BIC = 35,425.80 vs. 36,131.92). Item parameter estimates (IPEs) showed substantial variation in items' discriminating power (0.94 to 2.18). Standard errors of the IPEs were small (threshold parameters mostly around 0.1; discrimination parameters 0.1 to 0.2), confirming the precision of the IPEs.
The GRM provides precise IPEs that will enable comparable scores from different subsets of items, and facilitate optimal selections of items to estimate patients' latent satisfaction level. Given the large calibration sample, the IPEs can be used in settings with limited resources (e.g., smaller samples) to estimate patients' satisfaction.
我们使用经典测试理论方法开发并验证了患者对癌症相关护理满意度（PSCC）量表。本研究应用项目反应理论（IRT）分析来确定项目水平的心理测量特性，促进短式量表的发展，并为 PSCC 的未来应用提供信息。
我们对来自 1296 名参与者（73%为女性；年龄 18-86 岁）的 PSCC 数据应用了单维 IRT 模型。拟合无约束等级反应模型（GRM）和拉什模型，以使用似然比（LR）检验和信息准则来估计模型比较的指标。我们计算了项目和潜在特质参数估计、类别和操作特征曲线，并测试了较好拟合模型的信息曲线。
GRM 比拉什模型更适合数据（LR=828，df=17，p<0.001）。与拉什模型相比，GRM 的对数似然值（-17390.38 比-17804.26）更大，AIC 和 BIC 更小（AIC=34960.77 比 35754.73；BIC=35425.80 比 36131.92）。项目参数估计（IPE）显示出项目区分能力的显著变化（0.94 到 2.18）。IPE 的标准误差较小（阈值参数大多在 0.1 左右；区分参数在 0.1 到 0.2 之间），确认了 IPE 的精度。
GRM 提供了精确的 IPE，这将使不同项目子集的可比分数成为可能，并有助于最佳选择项目来估计患者的潜在满意度。鉴于较大的校准样本，IPE 可用于资源有限的环境（例如，较小的样本）来估计患者的满意度。
