Risk time splitting for improved estimation of screening programs effect on later mortality
通过风险时间拆分改进筛查项目对后续死亡率影响的估计AI 解读 ↓
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通过风险时间拆分改进筛查项目对后续死亡率影响的估计
- 研究问题
- 筛查效应存在延迟、邀请前已发病者难以受益时，如何区分死亡来源，并充分利用数据估计筛查对后续死亡率的影响？
- 方法与思路
- 作者利用历史诊断至死亡时间估计既往病例的死亡占比，设置泊松回归偏置项并调整协变量，同时加入最大似然估计。
- 材料陈述的贡献
- 摘要称文章详细解释已有风险时间拆分方法并扩展估计；挪威和丹麦应用中的自助法区间较其他精细死亡率方法窄。
- 阅读关注点
- 应核对历史生存信息向筛查期外推的依据、病例占比与偏置项的对应关系，以及协变量调整和自助法如何处理估计误差。
- 阅读边界
- 这是预印本，同行评审及期刊接收未核实；摘要未充分说明因果识别条件，区间更窄本身不能证明偏差更小或覆盖正确。
原文摘要
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.