Addressing immortal time bias in precision medicine: Practical guidance and methods development
比较了多种消除永恒时间偏倚的方法，提出基于多重插补的新方法，并通过真实案例提供操作指南，帮助研究者生成无偏的真实世界证据。
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. STUDY SETTING AND DESIGN: 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. DATA SOURCES AND ANALYTIC SAMPLE: 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. PRINCIPAL FINDINGS: 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.