动态治疗方案的反向概率加权估计意味着在连续的多重分配随机试验中缺少数据:一个模拟研究研究
Jessica Xu1, Robert K Mahar2,3,4, Katherine J Lee3,5
1Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia. jessicax2@student.unimelb.edu.au.
Trials
|January 31, 2026
概括
多重归算 (MI) 显示出相比完整病例分析 (CCA) 的最小偏差和略低的标准误差,用于估计顺序多重分配随机试验 (SMART) 中的动态治疗方案 (DTR) 平均值. 这一发现对于处理复杂的临床试验设计中缺少的数据至关重要.
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
- 医疗保健服务研究 医疗服务研究
背景情况:
- 动态治疗方案 (DTR) 在各种疾病中个性化了顺序治疗决策.
- 顺序多重分配随机试验 (SMART) 通过多阶段随机化评估和优化DTR.
- 反向概率加权 (IPW) 常用于估计SMART中的DTR平均值,但缺少的数据是一个挑战.
研究的目的:
- 评估完整案例分析 (CCA) 和多重归算 (MI) 在处理缺失数据的性能,以估计使用IPW的DTR平均结果,在两阶段的SMART中.
- 在各种缺失数据场景和比例下比较CCA和MI的偏差和标准误差.
主要方法:
- 模拟了1000个数据集,每个数据集有400名参与者,基于两阶段的SMART设计,非响应者被重新随机化.
- 估计四个DTR意味着使用IPW,在m-DAG定义的缺失数据场景下评估CCA和MI的性能.
- 评估缺失数据比例 (20%,40%) 和依赖于第一阶段的结果,第二阶段的治疗和最终的结果.
主要成果:
- 在大多数场景中,心脏病发作表现出最小的偏差,除了缺乏1阶段的中间结果,这取决于基线变量和1阶段的治疗.
- 当数据缺失取决于其他变量 (例如,根据第一阶段结果缺失第二阶段治疗) 时,CCA显示出比MI更大的偏差.
- 经验标准误差是可比的,MI通常产生略低的值.
结论:
- 对于所研究的原型 SMART 设计,由于偏差最小和标准误差略低,MI 通常优先于 CCA 来通过 IPW 估计 DTR 平均结果.
- 在处理缺失的数据复杂性方面,MI被证明是有效的,这些复杂性与 SMART 中的顺序随机化和中间结果依赖性有关.
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