观察性临床研究中的表型定义的变化:对三种条件的审查
Azza Shoaibi1, Anna Ostropolets1, James Weaver1
1Janssen Research & Development, Titusville, NJ, USA.
概括
现型算法 (PA) 在研究阿尔茨海默病,严重抑郁症和肺动脉高血压方面的研究中显示出显著的差异. 这些变化会影响患者的识别和发病率,突显了研究透明度的必要性.
科学领域:
- 健康研究方法的方法论.
- 观测数据库研究研究观察数据库研究.
- 生物统计学 生物统计学
背景情况:
- 表型算法 (PA) 对于在观察性研究中识别患者队列至关重要.
- 在PA定义中的异质性可能导致研究结果的变化.
- 阿尔茨海默病 (AD),主要抑郁症 (MDD) 和肺动脉高血压 (PAI) 是各种患者群体的复杂疾病.
研究的目的:
- 评估用于阿尔茨海默病 (AD),主要抑郁症 (MDD) 和肺动脉高血压 (PAI) 研究的表型算法 (PA) 的异质性.
- 评估PA变化的影响患者队列重叠和发病率估计.
- 识别跨研究报告和文档中的不一致性.
主要方法:
- 在AD,MDD和PAI中对49个复制的表型算法 (PAs) 的系统审查.
- 在六个观察数据库中分析PA变化及其对患者识别的影响.
- 对主要条件代码和纳入标准的报告完整性的评估.
主要成果:
- 在49个审查的PAs中观察到显著的异质性 (AD为13个,MDD为23个,PAI为13个).
- 各种PA确定了不同的患者队列,导致发病率的实质性异质性.
- 经常缺乏可复制性的全面文档.
结论:
- 现型算法的异质性显著影响患者队列识别和在AD,MDD和PAI研究中的发病率估计.
- 缺乏标准化和透明的PA报告阻碍了研究的可重复性.
- 促进稳健和透明的研究实践对于可靠的观测数据库研究至关重要.
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