考虑将现实世界数据作为对比队列组合到单臂试验的考虑:对评估异质性的模拟研究
Daniel Backenroth1, Trevor Royce2, Jose Pinheiro1
1Janssen Research & Development, Titusville, USA.
BMC medical research methodology
|August 24, 2023
概括
汇集现实世界数据 (RWD) 对罕见疾病研究至关重要. 调整后的Cochran's Q测试为聚合的RWD提供了强大的异质性评估,匹配单个参与者数据方法,并优于标准测试.
科学领域:
- 精准医学和罕见疾病研究.
- 现实世界数据 (RWD) 聚合方法.
- 评估异质性的统计方法.
背景情况:
- 精准医学针对的是罕见的,基因组定义的种群,需要RWD聚合.
- 在聚合RWD中评估异质性是复杂的,特别是比较真实世界的比较群 (rwCC) 与单臂试验 (SAT).
- 开发了一个RWD聚合的方法框架,重点关注rwCC和小数据集的新型异质性评估.
研究的目的:
- 开发一个方法框架,用于汇集RWD,特别是用于rwCC的用例.
- 模拟和评估在聚合的RWD中对异质性评估的新方法,特别是对于小型数据集.
主要方法:
- 建立了一个框架,包括预规范,数据集资格评估和结果分析,包括异质性评估.
- 进行了模拟,以评估二元响应结果的异质性,将SAT与两个rwCC进行比较.
- 方法包括标准的元分析,调整的Cochran的Q测试,并直接比较从rwCCs个人参与者数据 (IPD).
主要成果:
- 调整的科克兰的Q测试和IPD方法在检测真实差异方面表现相同,性能优于标准的科克兰的Q测试.
- 在零场景中,异质性测试中的低统计能力导致了1型错误膨胀.
- 在替代场景中,不足的异质性测试导致了2型错误和对治疗效应的低估.
结论:
- 开发了一种方法框架,用于将RWD组合起来,为SAT创建rwCC.
- 调整后的科克兰Q测试提供了与IPD异质性测试相比的统计能力.
- 量化异质性测试应用于数据集选择后的描述性测试,增强RWD聚合,以使瘤患者受益.
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