对COVID-19治疗有效性的观察性医院研究中的方法偏差:陷和潜在的潜在陷
Oksana Martinuka1, Derek Hazard1, Hamid Reza Marateb2,3
1Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany.
竞争风险和不朽时间偏差等方法学偏差显著扭曲了COVID-19治疗有效性研究. 通过试验模拟来解决这些偏见,可以提高现实世界观测数据分析的准确性.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 现实世界的观察性研究对于评估治疗有效性至关重要,但容易产生方法学偏见.
- 常见的偏见包括竞争风险,不朽时间偏见和混偏见,这些偏见可以扭曲治疗效果估计.
- 在COVID-19等疾病中,准确评估治疗有效性对于明智的临床决策至关重要.
研究的目的:
- 在观察性研究中讨论和评估竞争风险,不朽时间偏差和混偏差的影响.
- 用COVID-19患者数据来证明这些偏见的影响.
- 提出试验模拟框架作为减轻这些偏见的解决方案.
主要方法:
- 利用住院COVID-19患者的观察数据 (2020年3月至2021年2月).
- 使用标准统计方法比较治疗有效性,这些方法忽略或部分考虑偏差.
- 模拟了一个目标试验,使用克隆-传感器-重量方法来解决偏差.
主要成果:
- 忽略了竞争的风险,并使用卡普兰-梅尔估计器,高估了住院死亡率 (45.6%接受治疗,5.9%未接受治疗).
- 在试验模拟框架内,加权的阿伦-约翰森估计器降低了估计的死亡率 (27.9%的治疗者与40.1%的未治疗者).
- 不朽时间偏差导致对治疗危险比率的低估.
结论:
- 未能解决竞争风险,不朽时间偏差和混偏差导致偏差的治疗效果估计.
- 朴素的卡普兰-梅尔方法产生了最有偏见的结果,高估了COVID-19患者的死亡率.
- 试验模拟框架有效地解决了多种方法学偏见,为现实世界证据分析提供了更可靠的方法.
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