Bayesian machine learning for complex missing data and causal inference with a focus on cardiovascular and obesity studies

用于复杂缺失数据和因果推理的贝叶斯机器学习，重点关注心血管和肥胖研究

• 批准号: 10563598
• 负责人: Michael J Daniels
• 金额: $ 54.83万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10563598
• 关键词: Address; Algorithms; Bayesian Method; Bayesian Modeling; Bayesian learning; Behavioral Mechanisms; Behavioral trial; Blood Pressure; Body Weight; Cardiovascular system; Caring; Clinical; Clinical Research; Clinical Trials; Cluster randomized trial; Code; Collaborations; Comparative Study; Complex; Computer software; Data; Data Element; Diabetes Mellitus; Electronic Health Record; Ensure; Ethics; Exhibits; Frequencies; Funding; Future; Hand; Heterogeneity; Individual; Intervention; Kenya; Literature; Malignant Neoplasms; Mediating; Mediation; Mediator; Mental disorders; Methodology; Methods; Modeling; National Heart, Lung, and Blood Institute; Obesity; Observational Study; Outcome; Paper; Pharmaceutical Preparations; Pharmacoepidemiology; Process; Property; Protocols documentation; Public Health; Recording of previous events; Research; Research Personnel; Risk; Sampling; Science; Structure; Time; Uncertainty; Weight; Weight maintenance regimen; Work; bariatric surgery; behavior change; behavioral study; cardiovascular health; cost; data complexity; data modeling; diabetogenic; flexibility; hypertension treatment; improved; interest; large datasets; longitudinal, prospective study; machine learning method; novel; novel strategies; primary outcome; prospective; randomized trial; secondary outcome; tool; usability; virtual

Project Summary This proposal will develop Bayesian machine learning approaches via Bayesian nonparametrics (BNP) to handle nonignorable missingness (in outcomes and covariates) and conduct causal inference for electronic health records (EHRs), to address missingness in multivariate longitudinal data, and for causal mediation problems. Missing data remains a problem in clinical studies and in particular, for studies using EHRs. In clinical studies, more e↵ort is spent to try to minimize the amount of missingness, but it still remains a problem and missingness is a constant issue (and less controllable) in studies based on EHRs. In addition, there has been limited work on the use of auxiliary information in EHRs that can enhance the ability to deal with missing data. Approaches for missingness in multivariate longitudinal data is underdeveloped and relevant across many clinical trials settings from cost e↵ectiveness analysis to incomplete time-varying auxiliary covariates (or confounders) to causal mediation to multiple outcomes of interest. The mechanisms of treatment e↵ectiveness are of particular interest in behavioral trials. Specifically, how do di↵erent processes mediate the e↵ect of an intervention? This can facilitate constructing future interventions. However, determining the causal e↵ect of such 'mediators' on outcomes is di"cult. We will develop new approaches to identify these e↵ects in the complex setting of cluster randomized trials for which little work has been done. For all these settings, a Bayesian approach is ideal as it allows one to appropriately characterize uncertainty about unverifiable assumptions (which are present in all these problems) and allows the flexibility of Bayesian nonparametric models. MCMC algorithms for BNP can sometimes converge slowly and can be untenable for large n. We will extend existing approaches to address both these complications which will be important for all the applications considered and in general, given the increasing size and complexity of data. The methods are motivated by several NHLBI funded studies, whose PI's are co-investigators on this proposal, and will be developed to help answer numerous important clinical questions including the mechanisms of behavior change in weight management and the impact of linkage (and engagement) to care on treatment e↵ectiveness for blood pressure outcomes. The methods will also help us evaluate potentially synergistic e↵ects when drugs with potential diabetogenic e↵ects are used concomitantly and whether the impact on cancer outcomes varies by di↵erent bariatric surgeries. The history of the the collaborations among the entire study team will help produce the best science and facilitate dissemination of our methodological and clinical findings. We will disseminate code for these methods (via the PI's github page and software papers) to ensure the methods will be readily usable by investigators involved in cardiovascular, obesity, diabetes, and cancer studies.

项目摘要 该提案将通过贝叶斯非参数（BNP）开发贝叶斯机器学习方法来处理 不可点击的丢失（在结果和协变量中）并为电子健康记录进行因果推断 （EHR），解决多元纵向数据中的缺失以及因果中介问题。丢失的 在临床研究，尤其是使用EHR的研究中，数据仍然是一个问题。在临床研究中，更多 e↵Ort用于试图最大程度地减少失踪量，但仍然是一个问题，遗失是 在基于EHR的研究中，恒定的问题（较少受控）。此外，工作有限 在EHR中使用辅助信息可以增强处理丢失数据的能力。方法 在许多临床试验环境中 从成本e显分析到不完整的时变辅助协变量（或混杂因素）到因果关系 有多种感兴趣的结果。治疗的机制在行为方面特别感兴趣 试验。特定的，如何介导干预措施的e介导？这可以促进构建 未来的干预措施。但是，确定这种“调解人”的因果关系是“邪教”。我们将 开发新的方法来识别这些e↵在群集随机试验的复杂环境中 很少的工作完成了。对于所有这些设置，贝叶斯的方法是理想的选择，因为它可以适当 表征有关无法验证的假设（所有这些问题中存在）的不确定性，并允许 贝叶斯非参数模型的灵活性。 BNP的MCMC算法有时会缓慢收敛，并且可以 对于大n来说是站不住脚的。我们将扩展现有的方法来解决这两种并发症，这将是 考虑到数据的规模和复杂性的增加，对于所有考虑的应用程序和通常的应用程序都很重要。 这些方法是由几项NHLBI资助的研究激励的，其PI是该提案的共同研究者， 并将开发以帮助回答许多重要的临床问题，包括行为机制 体重管理的变化以及连锁（和参与度）对治疗的影响 用于血压结果。这些方法还将帮助我们评估药物时潜在的协同作用。 伴随潜在的糖尿病生成e↵Acnects以及对癌症结果的影响是否会产生 不同的减肥手术。 整个研究团队中合作的历史将有助于生产最好的科学和 促进我们的方法论和临床发现。我们将为这些方法传播代码 （通过PI的GitHub页面和软件论文）确保调查人员很容易使用这些方法 包括在心血管，肥胖，糖尿病和癌症研究中。