Giannakeas Vasily, Sopik Victoria, Narod Steven
Women's College Research Institute, Toronto, Ontario, Canada.
Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
Clin Epidemiol. 2020 Oct 27;12:1161-1169. doi: 10.2147/CLEP.S267584. eCollection 2020.
The degree of confidence one should place on non-randomised observational trials studies which estimate the benefit of screening depends on the validity of the analytic method employed. As is the case for all observational trials, screening evaluation studies are subject to bias. The objective of this study was to create a simulated data set and to compare four analytic methods in order to identify the method which was the least biased in terms of estimating the underlying hazard ratio.
We simulated a cohort of 100,000 women who were accorded US national rates of breast cancer incidence and breast cancer mortality over their lifetime. We assigned at random one-half of them to initiate mammography screening between ages 50 and 60. We used four different analytic approaches to estimate the hazard ratio under a null model (true HR = 1.0) and under a protective model (true HR = 0.80). Two models used the entire data set (with and without including mammography as a time-dependent covariate) and two models invoked matching of screened women with unscreened women (with and without excluding of women who had a mammogram after study initiation). For each of the four analytic methods, we compared the observed hazard ratio with the true hazard ratio. We considered an analytic method to be valid if the observed hazard ratio was close to the true hazard ratio.
Two simple analytic methods generated biased results that led to spurious protective effects observed when none was there. The least biased method was based on matching screened and unscreened women and which emulated a randomized trial design, wherein the unexposed control had no mammogram prior to study entry, but she was not excluded or censored if she had a mammogram after the index date.
There is no single ideal method to analyze observational data to evaluate the effectiveness of screening mammography (ie, which generates an unbiased estimates of the underlying hazard ratio) but designs which emulate randomised trials should be promoted.
人们对估计筛查益处的非随机观察性试验研究的置信程度，取决于所采用分析方法的有效性。与所有观察性试验一样，筛查评估研究也存在偏倚。本研究的目的是创建一个模拟数据集，并比较四种分析方法，以确定在估计潜在风险比方面偏倚最小的方法。
我们模拟了一个由100,000名女性组成的队列，她们一生中的乳腺癌发病率和乳腺癌死亡率符合美国国家水平。我们随机将其中一半女性分配为在50至60岁之间开始进行乳腺钼靶筛查。我们使用四种不同的分析方法，在零假设模型（真实风险比 = 1.0）和保护模型（真实风险比 = 0.80）下估计风险比。两种模型使用整个数据集（包括和不包括将乳腺钼靶作为时间依存协变量），另外两种模型对筛查女性和未筛查女性进行匹配（包括和不排除在研究开始后进行过乳腺钼靶检查的女性）。对于这四种分析方法中的每一种，我们将观察到的风险比与真实风险比进行比较。如果观察到的风险比接近真实风险比，我们就认为该分析方法是有效的。
两种简单的分析方法产生了有偏的结果，导致在实际上不存在保护作用时却观察到了虚假的保护作用。偏倚最小的方法是基于对筛查女性和未筛查女性进行匹配，并模拟随机试验设计，即未暴露的对照在研究入组前未进行乳腺钼靶检查，但如果她在索引日期后进行了乳腺钼靶检查，则不将其排除或截尾。
不存在单一的理想方法来分析观察性数据以评估乳腺钼靶筛查的有效性（即产生对潜在风险比的无偏估计），但应推广模拟随机试验的设计。
