QualityMetric Incorporated, LLC, Johnston, RI, USA.
University of Copenhagen, Copenhagen, Denmark.
Qual Life Res. 2023 May;32(5):1267-1276. doi: 10.1007/s11136-022-03172-5. Epub 2022 Jul 23.
Thresholds for meaningful within-individual change (MWIC) are useful for interpreting patient-reported outcome measures (PROM). Transition ratings (TR) have been recommended as anchors to establish MWIC. Traditional statistical methods for analyzing MWIC such as mean change analysis, receiver operating characteristic (ROC) analysis, and predictive modeling ignore problems of floor/ceiling effects and measurement error in the PROM scores and the TR item. We present a novel approach to MWIC estimation for multi-item scales using longitudinal item response theory (LIRT).
A Graded Response LIRT model for baseline and follow-up PROM data was expanded to include a TR item measuring latent change. The LIRT threshold parameter for the TR established the MWIC threshold on the latent metric, from which the observed PROM score MWIC threshold was estimated. We compared the LIRT approach and traditional methods using an example data set with baseline and three follow-up assessments differing by magnitude of score improvement, variance of score improvement, and baseline-follow-up score correlation.
The LIRT model provided good fit to the data. LIRT estimates of observed PROM MWIC varied between 3 and 4 points score improvement. In contrast, results from traditional methods varied from 2 to 10 points-strongly associated with proportion of self-rated improvement. Best agreement between methods was seen when approximately 50% rated their health as improved.
Results from traditional analyses of anchor-based MWIC are impacted by study conditions. LIRT constitutes a promising and more robust analytic approach to identifying thresholds for MWIC.
个体内有意义变化阈值（MWIC）对于解释患者报告的结局测量（PROM）很有用。转换评分（TR）已被推荐作为建立 MWIC 的锚点。用于分析 MWIC 的传统统计方法，如平均变化分析、接收者操作特征（ROC）分析和预测建模，忽略了 PROM 评分和 TR 项目中的地板/天花板效应和测量误差问题。我们提出了一种使用纵向项目反应理论（LIRT）对多项目量表进行 MWIC 估计的新方法。
一个用于基线和随访 PROM 数据的分级响应 LIRT 模型被扩展为包括一个测量潜在变化的 TR 项目。TR 上的 LIRT 阈值参数确定了潜在度量上的 MWIC 阈值，从中估计出观察到的 PROM 得分 MWIC 阈值。我们使用一个具有不同评分改善幅度、评分改善方差和基线-随访评分相关性的基线和三个随访评估的示例数据集，比较了 LIRT 方法和传统方法。
LIRT 模型对数据拟合良好。LIRT 对观察到的 PROM MWIC 的估计值在 3 到 4 个评分改善点之间变化。相比之下，传统方法的结果从 2 到 10 点变化——与自我评定改善的比例密切相关。当大约 50%的人认为自己的健康状况有所改善时，两种方法之间的一致性最佳。
基于锚定的 MWIC 的传统分析结果受到研究条件的影响。LIRT 构成了一种有前途且更稳健的分析方法，用于确定 MWIC 的阈值。
