模拟A / B测试与SMART设计的LLM驱动的患者参与,以弥补预防性护理差距
Sanjay Basu1, Dean Schillinger2, Sadiq Y Patel3,4
1Clinical Product Development, Waymark, San Francisco, CA, USA. sanjay.basu@waymarkcare.org.
NPJ digital medicine
|November 18, 2024
概括
顺序多重分配随机试验 (SMART) 提供了一个比传统的A/B测试更具成本效益的方法来个性化患者接触. 智能试验在检测异质治疗效果方面表现出卓越的力量,特别是在参与的后期阶段.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 生物统计学 生物统计学
- 卫生沟通健康沟通
背景情况:
- 人口健康倡议经常使用外展来解决预防护理方面的差距,比如过期的查和免疫接种.
- 针对不同患者群体个性化推广信息是很困难的,因为传统的A / B测试需要大样本大小来实现有限的消息变化.
研究的目的:
- 为了比较A / B测试的统计能力和错误阳性率与顺序多重分配随机试验 (SMART) 进行个性化患者沟通.
- 评估A/B测试和SMART设计在患者参与策略中的成本效益和净益.
主要方法:
- 微模拟被用来模拟和比较A/B测试和SMART设计.
- 分析考虑了各种效应大小和样本大小,以评估统计能力和假阳性率.
- 在不同的模拟场景中评估了成本效益和净收益.
主要成果:
- 顺序多重分配随机试验 (SMART) 在所有模拟场景中显示出更好的成本效益和净收益.
- 在较后的随机化阶段,SMART设计在检测异质治疗效应 (HTEs) 方面表现出卓越的性能.
- A/B测试需要更大的样本大小,并且在细微的消息优化中效率较低.
结论:
- 与传统的A/B测试相比,SMART试验是开发个性化患者沟通的更有效和更具成本效益的方法.
- 这些发现支持在人口健康倡议中使用SMART设计,旨在优化患者参与和资源配置.
- 随着患者群体在外展期间变得更加均,SMART在检测HTEs方面的优势变得更加明显.
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