一个系统审查的统计方法采用分析后续数据在队列多个随机对照试验的统计方法
Hina Narzari1, Nilima Nilima1, Venugopalan Y Vishnu2
1Biostatistics, All India Institute of Medical Sciences, New Delhi, New Delhi, IND.
Cureus
|February 5, 2024
概括
队列多重随机对照试验 (cmRCT) 与传统的RCT相比具有优势,但需要特定的分析方法. 本综述考察cmRCT分析策略,强调需要清晰和适当的技术,如工具变量 (IV) 或更复杂的平均因果效应 (CACE) 分析.
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
- 流行病学 流行病学
背景情况:
- 队列多重随机对照试验 (cmRCTs) 解决了传统随机对照试验 (RCTs) 的局限性.
- 由于缺乏标准化的统计方法,分析cmRCT数据仍然存在挑战.
- 这种变异性使得跨研究结果的比较变得复杂.
研究的目的:
- 审查和综合用于队列多重随机对照试验 (cmRCTs) 的各种统计分析方法.
- 提供自2010年设计开始以来cmRCT分析方法的概述.
主要方法:
- 在主要数据库中进行了系统的文献搜索,包括PubMed,Cochrane图书馆,EMBASE,JSTOR,Scopus,MEDLINE和ClinicalTrials.gov.gov.
- 确定并选了详细描述cmRCT数据统计分析的全文文章.
- 11项符合条件的研究被选中用于数据提取.
主要成果:
- 十一项研究符合纳入标准,所有研究均来自高收入国家,表明其他地区的利用不足.
- 治疗意图 (ITT) 分析被普遍采用;四项研究使用了仪器变量 (IV) 或更复杂的平均因果效应 (CACE) 分析.
- 大多数研究中都注意到术语的不一致使用 (随机化与随机选择) 和未指定的样本大小计算.
结论:
- 对于研究人员来说,RCT和cmRCT设计之间的清晰区别是必不可少的.
- 了解固有的设计差异对于解决潜在偏见至关重要.
- 建议采用先进的方法,如IV或CACE分析,以适当地减轻cmRCT的偏差.
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