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.

翻译：评估筛查项目的需求十分迫切，但分析人群筛查数据常因筛查效果的延迟性而变得复杂。在癌症筛查中，只有新发且尚未临床确诊的病例可能通过早期治疗从筛查中获益。因此，筛查后的死亡率数据应基于精细化死亡率进行分析，即根据首次筛查邀请前后的诊断时间对病例进行区分。历史上，精细化死亡率通常通过从现有数据中选择对照组来分离因果效应。虽然这种方法能提供有效估计，但由于忽略大部分可用数据，研究精度受到限制。在2014年的《英国医学杂志》中，Weedon-Fekjær等人采用了一种新的估计方法，利用了所有可用的挪威乳腺X线筛查数据。该方法通过历史筛查前数据（从临床诊断到死亡的时间）来估计若无筛查效应时，预期基于首次筛查邀请前发病病例的筛查后死亡率比例。利用这一预期比例，在泊松回归中添加偏移量以校准预期病例数，随后在调整相关协变量的基础上估计筛查效应。尽管该方法提高了研究精度，但尚未被便捷获取和广泛采用。本文详细阐释该方法，补充极大似然估计，并为其广泛应用奠定基础。将该方法应用于挪威和丹麦数据时，自助法置信区间明显窄于其他精细化死亡率方法所得的区间，对于逐步推行的挪威筛查项目尤为显著。