通过结合行政和医院死亡数据来估计整体生存率:一个方法上的挑战
Pierre-Yves Cren1,2,3, Clémence Leguillette4, Franck Craynest5
1Department of Medical Oncology, Centre Oscar Lambret, Lille, France. p-cren@o-lambret.fr.
European journal of epidemiology
|October 21, 2025
概括
使用法国统计和经济研究院 (INSEE) 的死亡数据与医院记录的使用可能会导致整体生存分析的偏差. 该EMR_INSEE_IMP方法为低死亡率的情况提供了可接受的偏差,但需要谨慎.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 统计和经济研究研究所 (INSEE) 现在提供死亡数据,促使对其用于整体生存分析的使用进行调查.
- 现有的医院数据 (EMR) 可能无法捕捉所有死亡事件,可能引入偏差.
研究的目的:
- 在纳入INSEE死亡数据时,量化整体生存估计中的偏差.
- 为了比较用于整合医院和国家死亡登记册数据的不同方法.
- 为研究中最佳使用这些数据提供建议.
主要方法:
- 进行了模拟研究,以评估不同死亡风险下的偏差,随访率的损失和INSEE死亡捕获率.
- 他们比较了三种方法:仅EMR,EMR与INSEE死亡 (EMR_INSEE) 和EMR与INSEE死亡,假设未被捕获的死亡是活着的 (EMR_INSEE_IMP).
- 通过检查生存曲线低估,危险比率偏差和I/II型错误膨胀来评估统计绩效.
主要成果:
- 在所有情景中,EMR_INSEE方法显著低估了整体存活率,并产生了偏差的危险比率,在所有情景中增加了错误.
- 在EMR_INSEE_IMP方法中,在低死亡率的环境中,偏差很低,特别是对随访的损失很低.
- 较高的死亡率或随访率的损失增加了EMR_INSEE_IMP方法的偏差风险,需要仔细考虑.
结论:
- 整合INSEE死亡数据需要谨慎的方法选择,以避免在总生存率估计中的重大偏差.
- 对于低风险人群来说,EMR_INSEE_IMP战略是一个可行的选择,但在高风险的临床场景中需要谨慎.
- 这项研究为研究人员提供了关键的见解,研究人员利用医院和国家生命统计数据进行生存分析.
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