当缺少响应时实施响应适应设计:归咎还是忽略?
Mia S Tackney1, Sofía S Villar1
1MRC-Biostatistics Unit, University of Cambridge, UK.
Statistical methods in medical research
|August 29, 2025
概括
处理数字健康试验中缺失的数据至关重要. 一个新的在线归算策略可以通过重新归算数据来提高准确性,从而影响勘探-开采权衡,并减少估计成功概率的偏差.
科学领域:
- 临床试验
- 数字健康
- 生物统计学
背景情况:
- 在临床试验中,缺少数据是一个重大挑战,尤其是在数字健康干预方面.
- 在数字健康试验中脱离往往会导致错过结果,经常错过非随机结果 (MNAR).
- 响应适应性设计需要在线处理缺失的数据,而不仅仅是审判后的归算.
研究的目的:
- 提出和评估一种新的在线归算策略,用于处理响应适应性临床试验中缺少的数据.
- 评估缺少数据策略对勘探和开采权衡的影响,以及估计成功概率的偏差.
- 研究MNAR机制对试验结果的影响.
主要方法:
- 开发了一种新的在线归算策略,允许重新归算以前的估计.
- 纳入确定性算法的截断和半随机算法的修改.
- 根据数字戒烟干预试验进行模拟研究.
主要成果:
- 选择的缺失数据策略对勘探和开采的权衡有很大影响,特别是不同部门的缺失率不同.
- 识别了响应适应性设计和缺失处理策略的有问题的组合.
- 通过乐观的抽样和MNAR机制,估计的成功概率可能会产生偏差.
结论:
- 在线归算策略对于在适应性数字健康试验中管理缺失的数据至关重要.
- 要避免偏差估计并保持试验完整性,必须仔细选择失踪处理方法.
- 这项研究强调了适应性设计,缺少数据和结果估计之间的复杂相互作用.
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