使用物件响应理论进行渐进性超核麻评分尺度版本的定量比较
Mohamed Gewily1, Elodie L Plan1, Elham Yousefi2
1Department of Pharmacy, Uppsala University, Uppsala, Sweden.
概括
一个由FDA验证的新的10项进步超核麻评分表 (PSPRS) 亚表,为评估治疗提供了更有效的工具. 这种尺度,结合项目响应理论,可以减少检测渐进性超核性麻 (PSP) 治疗效果所需的研究规模.
科学领域:
- 神经科学是一个神经科学.
- 临床评估 临床评估
- 生物统计学 生物统计学
背景情况:
- 渐进性超核性 (PSP) 是一种具有挑战性的神经退行性疾病,需要强有力的评估方法.
- 28项进步超核麻评分表 (PSPRS) 是标准的临床结果评估.
- 美国食品和药物管理局 (FDA) 提出的10项子规模提供了一个潜在的替代方案.
研究的目的:
- 通过物品响应理论,量化比较完整的PSPRS和FDA提出的10项子级的心理测量特性.
- 为PSP开发一种疾病进展模型.
- 评估不同研究设计和分析选项的效率,以检测治疗效应.
主要方法:
- 分析了来自四项干预试验和两个注册表的979名患者的数据.
- 项目响应理论 (IRT) 用于估计项目信息性和疾病进展.
- 进行了模拟,以比较不同尺度,试验设计和分析方法在检测治疗效果方面的功率.
主要成果:
- 完整的PSPRS表现出较低的项目相关性 (r=0.17±0.14),一些项目没有相关性.
- 美国食品和药物管理局选择的10项子量表显示了更高的相关性 (r=0.35±0.14) 并形成了纵向IRT进展模型的基础.
- 模拟表明,使用纵向项目信息,而不是治疗结束总分数,可以将检测疾病修饰效应所需的研究规模减半.
结论:
- 使用FDA选择的PSPRS项目进行纵向物品反应模型是评估PSP治疗的有希望的进步.
- 这种方法在临床试验中提高了进展性超核性麻的效率.
- 这些发现支持在PSP研究中使用FDA子尺度进行更有效和潜在的小规模临床试验.
相关概念视频
Self-Report Tests of Personality
327
Self-report inventories are objective personality assessments that use multiple-choice items or numbered scales, typically ranging from 1 (strongly disagree) to 5 (strongly agree). They are often called Likert scales after Rensis Likert. These inventories are widely used due to their ease of administration and cost-effectiveness. One of the most prominent examples is the Minnesota Multiphasic Personality Inventory (MMPI), initially developed in the 1940s to assess abnormal personality traits.
327
Friedman Two-way Analysis of Variance by Ranks
168
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
168
Ordinal Level of Measurement
23.2K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
23.2K
Ratio Level of Measurement
17.5K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated....
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated....
17.5K


