[论文解读] Risk time splitting for improved estimation of screening programs effect on later mortality

Harald Weedon-Fekjær, Elsebeth Lynge+1 more|arXiv (Cornell University)|Mar 11, 2026

Global Cancer Incidence and Screening被引用 0

一句话总结

本文通过风险时间切分和极大似然法，在癌症筛查前的数据基础上利用所有可用信息，改进并澄清了一种 refined mortality estimation 方法，在挪威和丹麦数据中实现了更窄的自举置信区间。

ABSTRACT

There is a great need for evaluating screening programs, but analysing data from population screening is often complicated by a delayed screening effect. In cancer screening, only new, not yet clinically diagnosed cases, might benefit from screening through earlier treatment. Hence, mortality data following screening should be analysed based on refined mortality, separating cases based on diagnosis before and after first screening invitation. Historically, refined mortality has been implemented by selecting comparison groups from the available data to disentangle the causal effect. While giving valid estimates, the ignorance of large parts of the available data has limited study precision. In BMJ 2014, Weedon-Fekjær et al. used a new estimation approach applying all the available Norwegian mammography screening data. The estimation uses historic pre-screening data on time from clinical diagnosis to death estimating the proportion of post-screening mortality which is expected to be based on cases incident before first screening invitation, in the absence of a screening effect. Utilizing this expected proportion of post-screening incident cases, Poisson regression offsets are added to align the expected number of cases. The screening effect is then estimated adjusting for relevant covariables. While the method increases study precision, it has not been easily available and widely adopted. We here explain the method in detail, add maximum likelihood estimation, and lay the foundation for widespread use. Applying the method on Norwegian and Danish data, bootstrap confidence intervals are considerably narrower than intervals seen using other refined mortality methods, especially for the gradually introduced Norwegian program.

研究动机与目标

• 推动评估筛查计划的必要性，即使死亡率存在滞后效应。
• 解释并形式化一种 refined mortality 方法，该方法使用筛查前数据来估计筛查后死亡率，而不丢弃数据。
• 提供极大似然估计框架及其实践实现。
• 在挪威和丹麦的筛查数据上展示该方法，以评估精度提升。

提出的方法

• 描述将诊断时间分为首次筛查邀请前后两类的 refined mortality 方法。
• 引入基于历史筛查前时间到死亡数据的偏移项的泊松回归，以使预计筛查后病例对齐。
• 将估计的偏移项并入回归，调整协变量以估计筛查效应。
• 给出该方法的极大似然估计公式。
• 将该方法应用于挪威和丹麦数据，并将自举置信区间与先前的 refined mortality 方法进行比较。

实验结果

研究问题

• RQ1 如何利用所有可用的筛查前数据来提高对筛查对死亡率影响估计的精度？
• RQ2 风险时间切分对筛查计划中死亡率效应估计的准确性与精度有何影响？
• RQ3 所提出的极大似然实现与现有的 refined mortality 方法在区间宽度方面有何比较？
• RQ4 来自挪威和丹麦的数据结果是否在逐步引入的计划中支持精度的提升？

主要发现

• 所提出方法的自举置信区间相较于其他 refined mortality 方法显著更窄。
• 该方法提升了研究的精度，尤其适用于逐步引入的筛查计划。
• 将该方法应用于挪威和丹麦数据，展示了在估计筛查对后期死亡率的影响方面的实际收益。