Département de mathématiques et de statistique, Université Laval, Canada.
Stat Med. 2011 Nov 10;30(25):3024-37. doi: 10.1002/sim.4334. Epub 2011 Aug 17.
The effect of a cancer screening program can be measured through the standardized mortality ratio (SMR) statistic. The numerator of the SMR is the observed number of deaths from the screened disease among participants in the screening program, whereas the denominator of the SMR is an estimate of the expected number of deaths in these participants under the assumption that the screening program has no effect. In this article, we propose a variance estimator for the denominator of the SMR when this expected number of deaths is estimated with Sasieni's method. We give both a general formula for this variance as well as formulas for specific disease incidence and survival estimators. We show how this new variance estimator can be used to build confidence intervals for the SMR. We investigate the coverage properties of various types of confidence intervals by simulation and find that intervals that make use of the proposed variance estimator perform well. We illustrate the method by applying it to the Québec Breast Cancer Screening program.
癌症筛查计划的效果可以通过标准化死亡率（SMR）统计数据来衡量。SMR 的分子是筛查计划参与者中筛查疾病的实际死亡人数，而 SMR 的分母是在假设筛查计划没有效果的情况下，这些参与者中预计的死亡人数的估计值。在本文中，当使用 Sasieni 方法估计预期死亡人数时，我们提出了 SMR 分母的方差估计量。我们给出了这个方差的一般公式，以及特定疾病发病率和生存率估计量的公式。我们展示了如何使用这个新的方差估计量来构建 SMR 的置信区间。我们通过模拟研究了各种类型的置信区间的覆盖性质，发现使用所提出的方差估计量的区间表现良好。我们通过将其应用于魁北克乳腺癌筛查计划来说明该方法。
