针对多变量顺序尺度结果和多种治疗的个性化治疗选择
Chathura Siriwardhana1, Bakeerathan Gunaratnam2, K B Kulasekera2
1Department of Quantitative Health Sciences, University of Hawaii John A. Burns School of Medicine, Honolulu, Hawaii, USA.
这项研究引入了一种新的方法,用于使用相关的顺序反应来个性化选择治疗. 它采用等级聚合来优化基于个体患者数据和偏好的治疗决策.
科学领域:
- 生物统计学 生物统计学
- 临床试验设计 临床试验设计
- 个性化医疗是个性化的医疗.
背景情况:
- 选择具有多个相关结果 (特别是顺序结果) 的最佳治疗方法具有挑战性.
- 现有的方法可能无法充分处理顺序响应尺度和相互依赖的复杂性.
研究的目的:
- 开发一种创新的,个性化的治疗选择方法,与相关的多个顺序反应相关联.
- 提供一个灵活的框架,容纳各种模型,治疗和反应,包括用于定制的权重.
主要方法:
- 利用从顺序结果的条件概率生成的排名列表.
- 引入了一种等级聚合技术,将多个等级列表结合起来,考虑列表内部和列表之间的相关性.
- 集成的响应权重用于患者和临床医生驱动的定制.
主要成果:
- 一项模拟研究证明了该方法在有限样本中的性能.
- 来自囊性纤维化和阿尔茨海默病临床试验的说明性例子展示了实际应用.
结论:
- 拟议的方法为个性化治疗选择提供了一种多功能和适应性的方法.
- 它有效地处理相关的多个顺序反应,增强复杂场景中的临床决策.
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