Pharmacometrics Research Group, Department of Pharmacy, Uppsala University, Box 580, 751 23, Uppsala, Sweden.
AAPS J. 2020 Dec 17;23(1):9. doi: 10.1208/s12248-020-00546-w.
Total score (TS) data is generated from composite scales consisting of several questions/items, such as the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS). The analysis method that most fully uses the information gathered is item response theory (IRT) models, but these are complex and require item-level data which may not be available. Therefore, the TS is commonly analysed with standard continuous variable (CV) models, which do not respect the bounded nature of data. Bounded integer (BI) models do respect the data nature but are not as extensively researched. Mixed models for repeated measures (MMRM) are an alternative that requires few assumptions and handles dropout without bias. If an IRT model exists, the expected mean and standard deviation of TS can be computed through IRT-informed functions-which allows CV and BI models to estimate parameters on the IRT scale. The fit, performance on external data and parameter precision (when applicable) of CV, BI and MMRM to analyse simulated TS data from the MDS-UPDRS motor subscale are investigated in this work. All models provided accurate predictions and residuals without trends, but the fit of CV and BI models was improved by IRT-informed functions. The IRT-informed BI model had more precise parameter estimates than the IRT-informed CV model. The IRT-informed models also had the best performance on external data, while the MMRM model was worst. In conclusion, (1) IRT-informed functions improve TS analyses and (2) IRT-informed BI models had more precise IRT parameter estimates than IRT-informed CV models.
总分（TS）数据是由多个问题/项目组成的综合量表生成的，例如运动障碍协会统一帕金森病评定量表（MDS-UPDRS）。最充分利用所收集信息的分析方法是项目反应理论（IRT）模型，但这些模型较为复杂，需要用到项目级数据，而这些数据可能并不存在。因此，TS 通常采用标准连续变量（CV）模型进行分析，而这些模型不尊重数据的有界性。有界整数（BI）模型确实尊重数据性质，但研究得还不够广泛。混合重复测量模型（MMRM）是一种替代方法，它需要的假设较少，并且可以无偏地处理缺失值。如果存在 IRT 模型，则可以通过 IRT 信息函数计算 TS 的期望均值和标准差，这允许 CV 和 BI 模型在 IRT 尺度上估计参数。本研究调查了 CV、BI 和 MMRM 分析模拟 MDS-UPDRS 运动子量表 TS 数据的拟合、对外部数据的性能以及参数精度（适用时）。所有模型都提供了准确的预测值和无趋势的残差，但 IRT 信息函数改善了 CV 和 BI 模型的拟合度。IRT 信息 BI 模型比 IRT 信息 CV 模型具有更精确的参数估计。IRT 信息模型在外部数据上的表现也最好，而 MMRM 模型的表现最差。总之，（1）IRT 信息函数可改善 TS 分析，（2）IRT 信息 BI 模型比 IRT 信息 CV 模型具有更精确的 IRT 参数估计。
