Service de Biostatistique-Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France.
Service de Biostatistique-Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France; Université de Lyon, Lyon, France; Université Lyon 1, Villeurbanne, France; CNRS UMR 5558, Laboratoire de Biométrie et Biologie Évolutive, Équipe Biostatistique Santé, Pierre-Bénite, France.
Arch Phys Med Rehabil. 2018 Sep;99(9):1776-1782.e9. doi: 10.1016/j.apmr.2018.02.017. Epub 2018 Apr 3.
To examine whether a Rasch analysis is sufficient to establish the construct validity of the Motor Function Measure (MFM) and discuss whether weighting the MFM item scores would improve the MFM construct validity.
Observational cross-sectional multicenter study.
Twenty-three physical medicine departments, neurology departments, or reference centers for neuromuscular diseases.
Patients (N=911) aged 6 to 60 years with Charcot-Marie-Tooth disease (CMT), facioscapulohumeral dystrophy (FSHD), or myotonic dystrophy type 1 (DM1).
None.
MAIN OUTCOME MEASURE(S): Comparison of the goodness-of-fit of the confirmatory factor analysis (CFA) model vs that of a modified multidimensional Rasch model on MFM item scores in each considered disease.
The CFA model showed good fit to the data and significantly better goodness of fit than the modified multidimensional Rasch model regardless of the disease (P<.001). Statistically significant differences in item standardized factor loadings were found between DM1, CMT, and FSHD in only 6 of 32 items (items 6, 27, 2, 7, 9 and 17).
For multidimensional scales designed to measure patient abilities in various diseases, a Rasch analysis might not be the most convenient, whereas a CFA is able to establish the scale construct validity and provide weights to adapt the item scores to a specific disease.
检验 Rasch 分析是否足以确立运动功能测量(MFM)的结构效度,并探讨对 MFM 项目评分进行加权是否会提高 MFM 的结构效度。
观察性横断面多中心研究。
23 个物理医学科、神经科或神经肌肉疾病参考中心。
6 至 60 岁的 Ch arcot-Marie-Tooth 病(CMT)、面肩肱型肌营养不良症(FSHD)或 1 型肌强直性营养不良症(DM1)患者(N=911)。
无。
比较在考虑的每种疾病中,MFM 项目评分的验证性因子分析(CFA)模型与修正多维 Rasch 模型的拟合优度。
CFA 模型对数据的拟合良好,与修正多维 Rasch 模型相比,无论疾病如何,均具有显著更好的拟合优度(P<.001)。在仅 6 项 32 项项目中(项目 6、27、2、7、9 和 17),DM1、CMT 和 FSHD 之间发现项目标准化因子负荷存在统计学显著差异。
对于旨在测量各种疾病中患者能力的多维量表,Rasch 分析可能不是最方便的,而 CFA 能够确立量表的结构效度并提供权重,以适应特定疾病的项目评分。