位置规模潜伏过程模型,用于重复的普通患者报告结果
Agnieszka Król1, Robert Palmér2, Jacob Leander2
1R&I Biometrics and Statistical Innovation, Respiratory & Immunology, BioPharmaceuticals R&D, AstraZeneca, Warsaw, Poland.
Statistics in medicine
|March 13, 2026
概括
这项研究引入了一种新的统计模型,用于分析临床试验中的每日患者报告结果 (PRO). 该模型捕捉了症状动态和变异性,提供了对疾病进展和治疗效果的见解.
科学领域:
- 生物统计学 生物统计学
- 临床试验方法论 临床试验方法论
- 纵向数据分析 纵向数据分析
背景情况:
- 患者报告的结果 (PROs) 对于评估临床试验中的生活质量至关重要.
- 传统的PRO分析往往忽略了它们的纵向和顺序特征.
- 电子数据收集使得频繁,每日PRO测量成为可能,需要先进的统计方法.
研究的目的:
- 开发和验证用于分析频繁的顺序纵向PRO数据的统计模型.
- 调查症状得分的动态及其随时间变化的变化.
- 在临床试验中评估治疗对症状进展的影响.
主要方法:
- 提出了一个位置尺度隐性过程模型,以捕捉顺序PROs的平均结构和可变性.
- 针对个体患者轨迹和短期变异性的共变量所包含的随机效应.
- 在R.中使用最大概率与近似蒙特卡洛近似估计了模型.
- 通过模拟验证了该方法,并将其应用于喘和COPD临床试验数据.
主要成果:
- 拟议的模型有效地分析了顺序PRO的动态,同时考虑了平均趋势和可变性.
- 在临床试验中证明了该模型能够评估治疗对症状进展和变异性的影响.
- 成功地将该方法应用于喘和COPD研究中的现实世界数据.
结论:
- 位置尺度潜伏过程模型为分析频繁的顺序纵向PRO数据提供了强大的框架.
- 这种方法通过捕获复杂的数据动态来增强对疾病进展和治疗疗效的理解.
- 经过验证的方法为临床试验分析提供了有价值的工具,特别是在呼吸系统疾病方面.
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