To Screen or Not To Screen: Prevention Decisions and Competing Risks

描述（由申请人提供）：常规癌症筛查可导致疾病的早期发现，从而预防与更晚期疾病相关的发病率并降低死亡率。然而，许多美国老年人患有合并症，存在竞争风险;因为筛查的许多好处，如降低死亡率，在未来积累，预期寿命较短的人不太可能实现这些好处。然而，目前的指南仅根据年龄提供建议，并且仅针对某些年龄，这在很大程度上是因为缺乏相关的试验数据。此外，可能无法进行 随机对照试验，能够解决当个人应该考虑停止常规筛查，因为高龄或共病人数增加。缺乏明确的建议或严格的证据可能会导致患者及其医生在做出这些决定时遇到困难。使用大型的，从医疗保险计划的观察数据集，因果推理的方法，我们建议模拟乳腺癌的乳房X线检查筛查的临床试验，结肠镜检查筛查CRC。我们2002-12年的数据来自20%的医疗保险按服务收费受益人的随机样本（A、B和D部分），包含有关筛查测试、癌症诊断、死亡、医疗支出和重要协变量的详细纵向信息。我们的三个目标是检查筛查对65岁以上受试者结局的影响，这些受试者具有不同数量的共病医疗条件：1）癌症检测; 2）生存; 3）医疗支出。我们将使用一系列方法，包括传统的生存和重复测量分析，以及最近开发的用于观察数据的因果分析技术，例如，具有逆概率加权的边际结构模型。我们还将探索使用工具变量和g公式模型的其他方法。虽然支持因果推理的方法近年来取得了相当大的进步，但它在临床，政策和比较有效性研究中的应用才刚刚开始。拥有超过2200万人年的数据，我们将有足够的能力检测我们结果中的临床相关差异。有了详细的纵向数据，我们将根据人口统计、临床、医生、地理和保险特征进行调整。这项研究提供了可以说是最好的机会，以评估替代癌症筛查策略的老年人与不同数量的共病医疗条件，从而告知政策制定者，临床医生和患者。

DESCRIPTION (provided by applicant): Routine cancer screening can lead to early detection of disease, thereby prevent morbidity associated with more advanced disease and reduce mortality. Many older Americans, however, have comorbid medical conditions that present competing risks; because many benefits of screening, such as reduced mortality, accrue in the future, persons with shorter life expectancies are less likely to realize the benefits. Current guidelines, however, provide recommendations based only on age, and only for some ages in large part because of the paucity of relevant trial data. Moreover, it may be infeasible to conduct randomized controlled trials that are able to address when individuals should consider stopping routine screening because of advanced age or increasing numbers of comorbid conditions. The absence of clear recommendations or rigorous evidence arguably contributes to the difficulty that patients and their physicians have when making these decisions. Using large, observational datasets from the Medicare program, and causal inference approaches, we propose to emulate clinical trials of mammography screening for breast cancer, and of colonoscopy screening for CRC. Our 2002-12 data from a 20% random sample of Medicare fee-for-service beneficiaries (Parts A, B, and D) contain detailed, longitudinal information on screening tests, cancer diagnoses, deaths, medical spending, and important covariates. Our three aims examine the impact of screening on outcomes for subjects age 65+ who have varying numbers of comorbid medical conditions: 1) cancer detection; 2) survival; and 3) medical spending. We will use a series of approaches including traditional survival and repeated measures analyses, and more recently developed techniques for causal analysis with observational data, e.g., Marginal Structural Models with inverse probability weighting. We also will explore additional approaches using instrumental variables and g-formula models. While the methodology supporting causal inference has advanced considerably in recent years, its use in clinical, policy, and comparative effectiveness research is only starting. With over 22 million person-years of data, we will have adequate power to detect clinically relevant differences in our outcomes. With detailed longitudinal data, we will adjust for a rich set of demographic, clinical, physician, geographic, and insurance characteristics. This study provides arguably the best opportunity to evaluate alternative cancer screening strategies among older Americans with varying numbers of comorbid medical conditions and thus inform policy makers, clinicians, and patients.