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一个连续的,多重分配,随机试验设计,具有量身定制的功能

Holly Hartman¹, Matthew Schipper², Kelley Kidwell²

¹Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA.

Statistics in medicine

|July 8, 2024

概括

这项研究介绍了一种使用连续定制函数的新型顺序多重分配随机试验 (SMART) 设计. 这种灵活的方法有效地估计动态治疗方案 (DTR),并有助于开发量身定制的疗法.

科学领域:

临床试验方法论临床试验方法论
生物统计学生物统计学
医疗保健中的机器学习

背景情况:

顺序多重分配随机试验 (SMARTs) 是适应性临床试验设计.
当前的SMART设计通常依赖于二进制定制变量,限制了灵活性.
开发动态治疗方案 (DTR) 需要有效的估计方法.

研究的目的:

引入一种新的SMART设计,使用连续定制函数,而不是二进制变量.
允许同时开发定制变量和估计DTRs.
为现有 SMART 设计提供更灵活,更有效的替代方案.

主要方法:

基于树的回归学习和Q学习用于DTR开发的应用.
与平衡随机的SMART和典型的SMART设计进行比较.
在SMARTs中,用于第二阶段治疗决策,使用连续结果.

主要成果:

拟议的具有定制功能的SMART设计有效地估计了DTRs.
与传统的SMART相比,这种设计在各种场景中更加灵活.
它消除了对预定义的二进制定制变量的必要性.

结论:

采用定制功能的SMART在临床试验设计中提供了更高的灵活性和效率.
这种方法促进了个性化治疗策略的开发.
该方法推进了适应性治疗选择的临床试验方法.

关键词:

在 Q-learning 中学习.智能智能 (SMARTs) 是一种智能化产品.临床试验中的临床试验.动态的治疗方案.裁功能裁功能定制变量是一个定制变量.基于树的强化学习学习.

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