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

利用多级生存数据和多种治疗进行因果推理的灵活贝叶斯方法

基本信息

批准号：
10056850
负责人：
Liangyuan Hu
金额：
$ 23.4万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-16 至 2021-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10056850
关键词：
Address Adoption Androgens Bayesian Method Bayesian Modeling Brachytherapy Caring Characteristics Clinical Clinical Practice Patterns Code Comparative Effectiveness Research Complex Data Databases Effectiveness Event Evidence based practice Health Healthcare Heterogeneity Individual Intervention Investigation Machine Learning Magnetic Resonance Imaging Malignant Neoplasms Malignant neoplasm of prostate Methodology Methods Modeling Modification Output Patient-Focused Outcomes Patients Performance Play Population Prostate Cancer therapy Public Health Radiation therapy Radical Prostatectomy Recommendation Research Personnel Role Source Statistical Data Interpretation Subgroup Techniques Testing Time Treatment Protocols Trees Variant Work base cancer epidemiology comparative complex data cost effectiveness data structure data tools deprivation design dissemination research effective therapy epidemiology study flexibility health care service high risk implementation research implementation trial improved interest large scale data novel novel strategies patient subsets regression trees simulation structured data survival outcome tool treatment arm treatment choice treatment effect user-friendly

项目摘要

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 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 heterogeneity 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.

项目概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，在公共卫生和医疗保健服务中发挥更大的作用在 CER 提出质疑的情况下，关于治疗和改进循证实践的采用。不适合直接实验或实施试验，其中不完全采用in- 当干预发生时，建议使用“现场数据”的因果推理工具来评估治疗效果。大型国家电子卫生数据库日益复杂，给统计分析和统计带来了挑战需求方法超越了传统的因果推理技术，传统上侧重于双向鉴于大规模数据中捕获的大量信息，治疗方案很少见。仅根据两种治疗来定义数据通常来自全国各地的治疗设施。尽管已经确定流行的双向工具具有很大的可变性。单一处理不适合多重处理设置，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情况我们提出的项目的首要目标是开发一种新颖且灵活的贝叶斯模型。然后我们充分利用聚类数据来估计多种治疗对生存的因果影响。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为了使因果估计不偏不倚，研究人员通常会采用实践中常用的方法。假设没有不可测量的混杂因素（UMC），尽管强烈建议采用二元处理，但有没有已知的实现或框架用于多种治疗和多水平生存的敏感性分析我们项目的第二个目标是开发和应用灵活且可解释的贝叶斯方法。评估因果估计对可能偏离无 UMC 假设的敏感性，这种方法能够衡量所需的未观察到的混杂因素的数量。改变观察到的治疗效果的方向我们的项目将首先应用开发的方法两个目标是从国家癌症中心抽取大量具有代表性的高风险局部前列腺癌人群数据库，评估三种流行治疗方案对生存的平均因果影响并评估我们还将估计治疗异质性和如何未测量的混杂因素可能改变因果结论。我们的方法将确定治疗有效或有害的不同患者亚组。有效性组成部分，为构建成本效益模型奠定基础，并提供证据进一步调查干预措施实施的差异和建议的修改为了促进我们工作的传播，我们将分享导致不同患者结果的治疗方法。通过 R 包的底层统计代码。