目的:比较常见的不朽时间调整方法的理论优势和局限性,提出了一种使用多重插补(MI)的新方法,并以实际案例研究为中心,为在精准医学评估中使用MI提供实践指导。
方法:方法比较,指导,和基于以往文献的现实案例研究。我们比较了里程碑分析,时间分布匹配,时间相关分析,和我们提出的MI应用程序。MI跨接指南(1)选择填补方法;(2)指定和应用填补模型;(3)进行比较分析和汇总估计。我们的案例研究使用匹配的队列设计来评估全基因组和转录组分析的总体生存益处。精准医学技术,与晚期癌症的常规治疗相比,并应用了时间分布匹配和MI。Bootstrap模拟具有对变化的数据错误和样本量的归因敏感性。
■案例研究使用了来自不列颠哥伦比亚省的基于人群的行政数据和单臂精密医学计划数据,加拿大为2012年至2015年的研究期间。
结果:虽然所描述的每种方法都可以减少不朽的时间偏差,MI提供了理论上的优势。与替代方法相比,MI最大程度地减少了信息损失,并更好地表征了关于不朽时间段真实长度的统计不确定性,避免错误的精度。此外,MI明确考虑了患者特征对不朽时间分布的影响,纳入标准和随访期定义不会无意中产生偏差评估。在现实世界的案例研究中,生存分析结果在MI和时间分布匹配方面没有实质性差异,但所有点估计值基于MI的标准误差均较高。平均估计的不朽时间在模拟中是稳定的。
结论:精准医学评估必须采用不朽的时间调整方法,决策级现实世界证据生成。MI是解决不朽时间偏见挑战的有前途的解决方案。
OBJECTIVE: To compare theoretical strengths and limitations of common immortal time adjustment methods, propose a new approach using multiple imputation (MI), and provide practical guidance for using MI in precision medicine evaluations centered on a real-world case study.
METHODS: Methods comparison, guidance, and real-world case study based on previous literature. We compared landmark analysis, time-distribution matching, time-dependent analysis, and our proposed MI application. Guidance for MI spanned (1) selecting the imputation method; (2) specifying and applying the imputation model; and (3) conducting comparative analysis and pooling estimates. Our case study used a matched cohort design to evaluate overall survival benefits of whole-genome and transcriptome analysis, a precision medicine technology, compared to usual care for advanced cancers, and applied both time-distribution matching and MI. Bootstrap simulation characterized imputation sensitivity to varying data missingness and sample sizes.
UNASSIGNED: Case study used population-based administrative data and single-arm precision medicine program data from British Columbia, Canada for the study period 2012 to 2015.
RESULTS: While each method described can reduce immortal time bias, MI offers theoretical advantages. Compared to alternative approaches, MI minimizes information loss and better characterizes statistical uncertainty about the true length of the immortal time period, avoiding false precision. Additionally, MI explicitly considers the impacts of patient characteristics on immortal time distributions, with inclusion criteria and follow-up period definitions that do not inadvertently risk biasing evaluations. In the real-world case study, survival analysis results did not substantively differ across MI and time distribution matching, but standard errors based on MI were higher for all point estimates. Mean imputed immortal time was stable across simulations.
CONCLUSIONS: Precision medicine evaluations must employ immortal time adjustment methods for unbiased, decision-grade real-world evidence generation. MI is a promising solution to the challenge of immortal time bias.