在子组分析和随机试验的二次结果中,可预防的偏差来源
Isaac Núñez1, Pablo F Belaunzarán-Zamudio2
1Department of Medical Education, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Vasco de Quiroga #15, Belisario Domínguez Sección XVI, Mexico City CP 14080, Mexico; Division of Postgraduate Studies, Faculty of Medicine, Universidad Nacional Autónoma de México, Mexico City, Mexico.
Contemporary clinical trials
|July 29, 2024
概括
在随机对照试验中,子组和二次结果分析可能会引入偏见,如果没有预先指定. 应用与初级分析相同的严格标准对于防止偏见和确保可靠的治疗疗效结论至关重要.
科学领域:
- 医学研究方法学 医学研究方法学
- 临床试验设计 临床试验设计
- 生物统计学 生物统计学
背景情况:
- 随机对照试验 (RCT) 是评估治疗疗效的黄金标准.
- 预先指定子组和二次结果分析对于防止p-hacking和其他偏见至关重要.
- 尽管预先规范,但这些分析仍然可以引入偏差,如果不像初级分析一样严格处理.
研究的目的:
- 识别和描述已发表的RCT中的子组和二次结果分析中偏差的来源.
- 为了说明这些偏见,使用现实世界的试验示例 (RECOVERY和START).
- 强调对所有试验结果的一致分析标准的重要性.
主要方法:
- 利用已发表的随机对照试验作为说明性示例.
- 采用因果定向非循环图 (DAG) 来概念化和解释偏差机制.
- 分析了特定类型的偏差,包括机会失衡,选择偏差和消耗偏差.
主要成果:
- 预后变量中的机会失衡可能会影响子组分析.
- 当子组变量影响试验参与或保留时,选择偏差就会出现.
- 由于后续数据不完整,在子组分析中可能会忽略收缩偏差.
- 预后变量的差异分布可能会影响二次结果分析.
结论:
- 亚组和二次结果分析可以显著影响治疗批准和临床决策.
- 一致地应用严格的分析标准,相当于初级分析,对于所有试验结果至关重要.
- 积极解决这些偏见可以确保医学研究结果的完整性和可靠性.
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