Evaluation of dynamic strategies of cancer care

项目摘要/摘要 在为癌症治疗做出最重要的决定时，临床医生自然会考虑患者的病情演变 特征和风险因素，并相应地量身定制治疗或诊断测试。换句话说，他们 考虑依赖于一个或多个依赖时间的协变量的演变的动态治疗策略 (例如，功能状态、虚弱、新的共病)，而不是静态治疗策略，后者不会。 尽管动态治疗策略与临床相关，但大多数研究继续比较静态 战略。这在一定程度上是因为，比较动态策略的随机临床试验可能特别 昂贵、耗时且效率低下。此外，动态策略的观察性研究需要 具体的方法，因为即使在没有不可测量的混杂的情况下，传统的估计也无法 在下列情况下进行因果解释：(I)存在可测量的时变预测因素，该因素也可以预测 随后的治疗，以及(Ii)过去的治疗预测随后的预后因素水平。这种治疗方法- 混杂反馈在生物医学研究中普遍存在。 这个应用程序的广泛目标是创建一个创新的研究计划，将来自不同领域的数据整合在一起 电子病历、行政索赔、流行病学队列等来源(以及未来 可穿戴健康技术和患者门户)使用先进的流行病学方法来研究动态 提供医疗保健的战略，并创建公开可用的分析工具，以加快发现和 支持更具战略性的癌症护理服务。 具体目标包括开放源代码编程、数据科学和高级流行病学方面的培训 方法：研究方法。这些技能将用于开发三个将使用不同数据源(传统的 流行病学队列，将肿瘤登记与索赔和电子医疗记录联系起来)，以解决 关于癌症预防、监测和治疗的问题： ·结直肠癌筛查的最佳动态策略是什么？ ·膀胱癌监测的最佳动态策略是什么？ ·对于仅有PSA复发的前列腺癌患者，最佳动态治疗策略是什么？ 这些项目将作为开发和微调研究所需的具体方法的支柱 卫生保健服务的动态策略：G-方法。具体地说，此应用程序将重点放在 边缘结构模型和参数g公式在临床肿瘤中的应用与推广 研究。

PROJECT SUMMARY/ABSTRACT In making the most important decisions for cancer care, clinicians naturally consider their patients' evolving characteristics and risks factors and tailor the treatments or diagnostic tests accordingly. In other words, they consider dynamic treatment strategies that depend on the evolution of one or more time-dependent covariates (e.g. functional status, frailty, new comorbidities) as opposed to static treatment strategies which do not. Despite the clinical relevance of dynamic treatment strategies, most research continues to compare static strategies. This is because, in part, randomized clinical trials comparing dynamic strategies can be particularly expensive, time-consuming, and inefficient. Additionally, observational studies of dynamic strategies require specific methods because even in the absence of unmeasured confounding, conventional estimates fail to have a causal interpretation when (i) there exists a measured time-varying prognostic factor that also predicts subsequent treatment, and (ii) past treatment predicts subsequent prognostic factor level. This treatment- confounder feedback is pervasive in biomedical research. This application's broad objective is to create an innovative research program that integrates data from diverse sources such as electronic medical records, administrative claims, epidemiologic cohorts (and in the future wearable health technology and patient portals) with advanced epidemiological methods to study dynamic strategies of health care delivery and create publicly available analytic tools to accelerate discovery and support a more strategic delivery of cancer care. The specific aims involve training in open-source programming, data science and advanced epidemiological methods. These skills will be used to develop three projects that will use different sources of data (a traditional epidemiological cohort, a linkage of tumor registries with claims and electronic medical records) to address questions on prevention, surveillance and treatment of cancer: • What is the best dynamic strategy for colorectal cancer screening? • What is the best dynamic strategy for bladder cancer surveillance? • What is the best dynamic strategy of treatment for prostate cancer patients with a PSA-only relapse? These projects will serve as a backbone to develop and fine tune the specific methods required to study dynamic strategies of health care delivery: the g-methods. Specifically, this application will focus on the application and dissemination of Marginal Structural Models and the parametric g-formula for clinical cancer research.