Bayesian machine learning for causal inference with incomplete longitudinal covariates and censored survival outcomes

项目摘要 由美国国家卫生研究院资助的人群队列研究，包括 多民族研究（ARIC）和多民族动脉粥样硬化研究（梅萨），广泛用于心血管疾病 研究并为心血管疾病（CVD）预防策略提供了基础知识， 公共卫生政策。跨多个队列的汇总数据为深入分析提供了独特的机会- 新出现的CVD研究问题，如触发启动的最佳血压阈值 年轻人的抗高血压治疗，这在此之前是不可能的。同时形成 创新研究的沃土，与汇总队列数据相关的方法学问题不能 用现有的统计方法有效地解决。主要有三个分析挑战。一是很多 离散或连续纵向变量具有各种缺失数据模式的缺失值。现有 方法要么容易受到误设偏倚的影响，要么不能提供插补的一致估计， 不信，不信。其次，当前的因果推理方法要么需要 对齐的测量时间点或关于因果途径形式的参数假设， 可以萨蒂斯艾德在复杂的纵向健康数据。第三，违反“顺序可解释性”假设 嵌入因果推理方法中可能是偏见的潜在来源。敏感性分析方法 与删失生存结局的时变混杂还不成熟。为了克服这些困难， 继承和改进统计和CVD研究，我们提出了一套可推广的统计方法， 机器学习我们建议开发一个可扩展的贝叶斯非参数（BNP）框架，以填补CON- 随机纵向协变量的连续或离散缺失，同时提供一致的不确定性区间，以及 通过敏感性分析解决非随机缺失机制。我们将应用开发的方法， 解决几个纵向CVD风险因素（如血压、胆固醇水平）的数据缺失问题 （具体目标1）;开发一种稳健且计算效率高的BNP因果推断方法（具体目标 2)和一个新的连续时间边际结构生存模型从贝叶斯的角度（具体目标3）， 研究和验证年轻人和体弱者的时变抗高血压治疗的生存影响 老年人;开发一种灵活和可解释的生存敏感性分析方法，以评估 不同程度的序贯不可测量混杂的因果效应估计（具体目标4）;以及创建 可用的R软件包的所有建议的方法，并开发教程论文和短期课程，以桥梁 理论和实践知识，并促进使用我们的方法（具体目标5）。

Project Summary Population cohort studies funded by the National Institute of Health, including the Atherosclerosis Risk in Com- munities (ARIC) Study and Multi-Ethnic Study of Atherosclerosis (MESA), are widely used in cardiovascular research and have provided fundamental knowledge for cardiovascular disease (CVD) prevention strategies and public health policies. Pooling data across multiple cohorts provides a unique opportunity for in-depth investiga- tions of emerging CVD research questions, such as optimal blood pressure threshold values triggering initiation of antihypertensive treatment for young adults, that heretofore would not have been possible. While forming a fertile ground for innovative research, the methodological issues associated with the pooled cohorts data cannot be as effectively addressed by existing statistical methods. There are three main analytic challenges. First, many discrete or continuous longitudinal variables have missing values with various missing data patterns. Existing methods either are susceptible to misspecification biases or do not provide coherent estimates of imputation un- certainty, and cannot handle missing not at random. Second, current causal inference methods either require aligned measurement time points or parametric assumptions about forms of causal pathways, neither of which can be satisfied in complex longitudinal health data. Third, violations of the “sequential ignorability” assumption embedded in causal inference methodology can be a potential source of bias. The sensitivity analysis methods for time-varying confounding with censored survival outcomes are underdeveloped. To overcome these chal- lenges and improve statistical and CVD research, we propose a suite of generalizable statistical methods utilizing machine learning. We propose to develop a scalable Bayesian nonparametric (BNP) framework to impute con- tinuous or discrete missing at random longitudinal covariates while providing coherent uncertainty intervals, and address the missing not at random mechanism via sensitivity analysis. We will apply the developed method to address missing data issues for several longitudinal CVD risk factors such as blood pressure, cholesterol levels (Specific Aim 1); to develop a robust and computationally efficient BNP causal inference method (Specific Aim 2) and a new continuous-time marginal structural survival model from a Bayesian perspective (Specific Aim 3) to study and validate the survival effects of time-varying antihypertensive treatments for young adults and the frail elderly; to develop a flexible and interpretable survival sensitivity analysis method to assess the sensitivity of the causal effect estimates to varying degrees of sequential unmeasured confounding (Specific Aim 4); and to create usable R software packages for all proposed methods and develop tutorial papers and short courses to bridge theoretical and practical knowledge and promote use of our methods (Specific Aim 5).