在对比疗效和干预安全研究中的高维倾向得分的方法审查中,发现与算法开发和稳定性相关的报告不完整
Guillaume Louis Martin1, Camille Petri2, Julian Rozenberg3
1Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, AP-HP, Hôpital Pitié Salpêtrière, Département de Santé Publique, Paris, France; Synapse Medicine, Bordeaux, France.
Journal of clinical epidemiology
|February 28, 2024
概括
高维倾向分数 (hdPS) 方法有助于解决现实数据中的混问题,但需要更好的报告. 这次审查发现,许多使用hdPS的研究方法缺乏透明度,影响了可靠性.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 二次数据分析对于现实世界的有效性和安全性研究至关重要.
- 由于二次数据库中未测量的变量导致的混是一个重大挑战.
- 高维倾向分数 (hdPS) 算法是为了使用代理变量减轻混而开发的.
研究的目的:
- 评估使用hdps算法研究的方法和报告质量.
- 评估hdPS在有效性和安全性比较研究中的应用和透明度.
主要方法:
- 2009年7月至2022年5月期间发表的研究的系统方法审查.
- 在PubMed和谷歌学者中进行的搜索.
- 使用ROBINS-I工具评估的偏差风险.
主要成果:
- 136项研究符合纳入标准,主要使用北美数据库.
- 在88%的研究中,hdPS是主要分析方法.
- 报告hdPS的方法通常是不完整的,只有8%遵守所有推项目.
- 60%的研究显示,整体偏差风险中等.
结论:
- 在报告hdPS研究方面需要显著改进.
- 在HDPS建设的方法选择中提高透明度对于可靠的研究至关重要.
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