Applying causal inference methods to improve estimation of the real-world benefits and harms of lung cancer screening

摘要 随机对照试验已经证明，低剂量计算机断层扫描可以显著减少 肺癌死亡率，尽管代价是相对较高的假阳性率和并发症 下游程序。然而，符合条件的试验人群和普通人群之间的系统性差异 肺癌筛查引起了人们对试验结果与指导发展和 在临床实践中推广肺癌筛查方案。尽管委员会明确提出了建议 美国预防服务工作组，肺癌筛查的接受率和依从性仍然很低。 几项研究已经记录了整个筛查过程中戏剧性和选择性的自然减员-其中 大约10%-20%的符合条件的个人接受了肺癌筛查，而在这些人中，只有大约40%-60%达到了 在15个月时与他们的年度筛查约会。当干预的好处和坏处在不同的地方不同时 亚群和有选择的自然减员，人口层面的利益和损害的平衡预计将 变化。因此，迫切需要更好地表征肺癌筛查的有效性 当应用于临床试验环境之外的个体时，低剂量计算机断层扫描。初级阶段 这项提案的目的是为肺癌筛查的益处和危害提供真实的证据 使用低剂量计算机断层扫描，明确考虑每一步人群的特征 筛选连续体，从符合筛选条件的人，到经过筛选的人，再到附着者。要解决这个问题 目标，我们将使用尖端的因果推理方法，包括试验传输和目标试验仿真 使用真实世界的数据，这可以避免潜在的与时间相关的偏差。来实施我们的建议 分析，我们还将利用来自随机国家肺部筛查试验的个人水平的数据 作为包括国民健康和访谈调查在内的四个真实世界的数据集，行为风险因素 监视调查(肺癌筛查模块)，20%的全国医疗保险索赔样本，以及 北卡罗来纳州肺部筛查登记处。这项研究的发现将产生关于 患者、提供者和患者可使用的真实环境中肺癌筛查的有效性 政策制定者。这项工作将加强政策制定者用来更新筛查的证据基础 建议和改进决策辅助工具，以支持与患者就筛查网络进行沟通- 分享决策过程中的利益。最终，这项工作将支持改善肺的交付的努力 在人口层面进行癌症筛查。

ABSTRACT Randomized controlled trials have demonstrated that low-dose computed tomography can substantially reduce lung cancer mortality, albeit at the cost of relatively high rates of false positives and complications from downstream procedures. However, systematic differences between trial and general populations eligible for lung cancer screening raise concerns about the relevance of trial findings for guiding the development and dissemination of lung cancer screening programs in clinical practice. Despite clear recommendations from the United States Preventative Services Task Force, lung cancer screening uptake and adherence remain low. Several studies have documented dramatic and selective attrition across the screening continuum – where about 10-20% of eligible individuals undergo lung cancer screening and of those, only about 40-60% are up-to- date with their annual screening at 15 months. When the benefits and harms of an intervention vary across subgroups and there is selective attrition, the balance of population-level benefits and harms is expected to change. As such, there is an urgent need to better characterize the effectiveness of lung cancer screening with low-dose computed tomography when applied to individuals outside of clinical trial settings. The primary objective of this proposal is to generate real-world evidence of the benefits and harms of lung cancer screening with low-dose computed tomography that explicitly considers the characteristics of populations at each step of the screening continuum, from the screening-eligible, to the screened, to the adherent. To address this objective, we will use cutting-edge causal inference methods, including trial transport and target trial emulation using real-world data, which can avoid the potential for time-related biases. To carry-out our proposed analyses, we will draw upon individual-level data from the randomized National Lung Screening Trial, as well as four real-world datasets including the National Health and Interview Survey, the Behavioral Risk Factors Surveillance Survey (Lung Cancer Screening Module), a 20% nationwide sample of Medicare claims, and the North Carolina Lung Screening Registry. Findings from this study will generate information about the effectiveness of lung cancer screening in real-world settings that can be used by patients, providers, and policymakers. This work will enhance the evidence base used by policymakers to update screening recommendations and refine decision aids to support communication with patients about screening net- benefits during shared decision-making. Ultimately, this work will support efforts to improve the delivery of lung cancer screening at the population level.