Flexible Bayesian approaches to causal inference with multilevel survival data and multiple treatments

项目摘要 将比较有效性研究(CER)与传播和实施研究相结合正在发挥作用 通过允许从业者做出明智的决定，在公共卫生和保健服务中发挥更大的作用 关于治疗和改进循证做法的采用。在CER提出问题的情况下 不适合直接进行实验或在不完全采用in- 为了评价治疗效果，推荐使用“fi-ELD数据”的因果推断工具。这个 大型国家电子健康数据库的复杂性增加给统计分析和 需求方法超越了传统的因果推理技术，传统上专注于双向推理技术。 不接受任何治疗。考虑到从大规模数据中捕获的丰富信息，治疗方案很少见 DefiNed只有两种治疗方法。这些数据通常是从全国各地的治疗设施中汇集而来的 在制度效应方面有相当大的变异性。尽管已经确定流行的双人计算机工具-- 任何处理都不适合于多处理设置，并且忽略了多级数据结构 可能会对治疗效果的估计产生偏差，但很少有人提出替代方法来处理这两种方法 并发症同时发生。我们提出的项目的第一个目标是开发一种新颖的、可扩展的贝叶斯方法(fifl 用聚集数据估计多种治疗对存活率的因果效应的方法。然后我们就完全 研究我们提出的方法在各种模拟场景中的运行特性并进行对比 它与实践中经常使用的方法相一致。为了使因果估计是公正的，研究人员通常会做出 假设没有不可测量的混杂(UMC)。虽然强烈建议使用二元治疗，但有 对于多个治疗和多水平生存的敏感性分析，目前还没有已知的实施或框架 数据。我们项目的第二个目标是开发和应用fl可扩展和可解释的贝叶斯方法 评估因果估计对可能偏离无UMC假设的敏感度 群集级和个人级。这种方法能够测量所需的未观察到的混杂程度。 为了改变观察到的治疗效果的方向，我们的项目将在fiRST中应用开发的方法 两个目标是从de-identified.中抽取具有代表性的高危局部性前列腺癌人群 国家癌症数据库，评估三种流行治疗方案对存活率的平均因果影响 并评估未经测量的混淆可能如何改变因果结论。我们还将评估治疗情况- 并确定治疗对其有效或有害的不同患者亚群。我们的方法 将建立效益部分，并为建立成本效益模型奠定基础； 并为进一步研究干预实施和Modifi阳离子的变化提供了证据 对导致不同患者结局的治疗建议。为了方便传播我们的工作， 我们将通过R包共享基本统计代码。

Project Summary Combining comparative effectiveness research (CER) and dissemination and implementation research is playing an increased role in public health and health care service by allowing practitioners to make informed decisions about treatments and improving adoption of evidence-based practices. In circumstances where CER questions do not lend themselves to direct experimentation or in implementation trials where incomplete adoption of in- tervention occurs, causal inference tools for “field data” are recommended for evaluating treatment effects. The increased complexities in large national electronic health databases pose challenges for statistical analyses and demand approaches beyond conventional causal inference techniques, which have traditionally focused on bi- nary treatment. Given the wealth of information captured in large-scale data, it is rare that treatment regimens are defined in terms of two treatments only. The data are typically pooled from treating facilities across the nation with considerable variability in the institutional effect. Although it has been established that popular tools for bi- nary treatment are inappropriate for the multiple treatment setting, and that ignoring the multilevel data structure can bias the estimate of the treatment effect, few alternative methods have been proposed to deal with both complications simultaneously. The first aim of our proposed project is to develop a novel and flexible Bayesian approach to estimating the causal effects of multiple treatments on survival with clustered data. We then fully investigate the operating characteristics of our proposed method in a variety of simulated scenarios and contrast it with approaches often used in practice. For causal estimates to be unbiased, researchers commonly make the assumption of no unmeasured confounding (UMC). Though highly recommended with binary treatment, there is no known implementation or framework for sensitivity analysis with multiple treatments and multilevel survival data. The second aim of our project is to develop and apply a flexible and interpretable Bayesian approach to assessing the sensitivity of causal estimates to possible departures from the assumption of no UMC, at both cluster- and individual-level. This approach is capable of gauging the amount of unobserved confounding needed to change the direction of the observed treatment effects Our project will apply the developed methods in the first two aims to a large representative high-risk localized prostate cancer population, drawn from the de-identified National Cancer Data Base, to evaluate the average causal effects of three popular treatment options on survival and evaluate how unmeasured confounding might alter causal conclusions. We also will estimate treatment het- erogeneity and identify distinct subgroups of patients for which a treatment is effective or harmful. Our methods will establish the effectiveness component and lay the groundwork for building the cost-effectiveness models, and provide evidence for further investigations of variations in intervention implementation and modifications in recommendations for treatments leading to different patient outcomes. To facilitate the dissemination of our work, we will share the underlying statistical code via an R package.