两种方法的比较,以确定物品特征函数和潜在变量时间课程,用于药量计物品响应模型
Leticia Arrington1, Mats O Karlsson2
1Department of Pharmacy, Uppsala University, P.O. Box 580, SE-751 23, Uppsala, Sweden.
The AAPS journal
|January 25, 2024
概括
这项研究比较了两种物品响应理论 (IRT) 方法用于药量学,发现两者在模拟疾病进展和药物效应方面都具有相似的性能. 同时方法提供了稍微更好的精度,而顺序方法提供了更强大的稳定性.
科学领域:
- 药物指标 (Pharmacometrics) 是一个指标.
- 项目响应理论 (IRT)
- 纵向数据分析 纵向数据分析
背景情况:
- 项目特征函数 (ICF) 和隐性变量时间过程建模在使用IRT的药量计学中至关重要.
- 现有的文献为这些估计提供了同时和顺序的方法.
- 这两种方法之间缺乏直接比较.
研究的目的:
- 在药量测量框架中,系统地比较同步和顺序IRT方法的性能.
- 评估它们在模拟疾病进展和药物效应方面的有效性.
- 在不同的进展率和样本大小下评估他们的行为.
主要方法:
- 从基于帕金森病进展标记计划 (PPMI) 数据的分级响应IRT模型中使用的项目参数进行模拟.
- 在具有和没有药物效应的条件下评估了同时和顺序的方法.
- 用缓慢进展/小样本大小和快速进展/大样本大小的场景评估性能.
主要成果:
- 这两种方法都表现出类似的性能,偏差低,对关键参数和药物效应假设测试的精度很好.
- 项目特征函数 (ICF) 参数被确定得很好,在快速进展下精确度提高.
- 观察到缓慢进展的药物效应的估计偏差,但与整体进展率相比较小.
结论:
- 同时和顺序的IRT方法都适用于药量测量建模,性能可比.
- 同时方法显示了略高的精度.
- 顺序方法为模型错误规范提供了更大的稳定性和模型构建中的实际优势.
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