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

项目摘要 由美国国立卫生研究院资助的人群队列研究，包括慢性阻塞性肺疾病的动脉粥样硬化风险 社区(ARIC)研究和动脉粥样硬化(MESA)多种族研究被广泛应用于心血管领域 研究并提供了心血管疾病(CVD)预防策略和 公共卫生政策。跨多个队列汇集数据为深入调查提供了独特的机会- 对新出现的心血管研究问题的看法，如触发启动的最佳血压阈值 对于年轻人的降压治疗，到目前为止这是不可能的。同时形成一个 创新研究的肥沃土壤，与集合队列数据相关的方法学问题不能 用现有的统计方法同样有效地解决问题。有三个主要的分析挑战。首先，很多人 离散或连续的纵向变量具有具有各种缺失数据模式的缺失值。现有 方法要么容易受到错误的fi阳离子偏向的影响，要么不能提供对归因错误的一致估计。 确定无疑，不能随意处理思念。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路径形式的参数假设，两者都不是 在复杂的纵向健康数据中可以满足fi。第三，违反“顺序可忽略”假设 嵌入到因果推理方法中可能是一个潜在的偏见来源。灵敏度分析方法 对于时变混杂与审查的生存结果是不发达的。为了克服这些障碍-- 为了延长和改进统计和CVD研究，我们提出了一套通用的统计方法，利用 机器学习。我们建议开发一种可扩展的贝叶斯非参数(BNP)框架来对计算结果进行归结。 在随机纵向协变量处连续或离散缺失，同时提供一致的不确定区间，以及 通过敏感度分析解决遗漏的非随机性机制。我们将把开发的方法应用于 解决血压、胆固醇水平等几个纵向心血管疾病风险因素的缺失数据问题 (SPECIfic Aim 1)；开发一种稳健且计算有效的fiBNP因果推理方法(SPECIfic Aim 2)和一个新的基于贝叶斯观点的连续时间边际结构生存模型(SPECIfic Aim 3) 研究和验证时变降压治疗对青壮年和体弱者的生存效果 建立一种fl可扩展和可解释的生存敏感性分析方法，以评估生存敏感性。 因果效应估计到不同程度的顺序未测量的混淆(规范fic目标4)； 为所有建议的方法提供可用的R软件包，并开发教程论文和短期课程来搭建桥梁 理论和实践知识，并促进使用我们的方法(规范fic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).