Theory and Methods for Causal Inference in Chronic Diseases

心血管疾病和艾滋病毒等慢性疾病在美国国内和全球都造成了巨大的健康和经济负担。随着最近的技术进步，慢性病研究企业正迅速变得数据密集型和数据驱动型。海量和复杂的数据为发现慢性病的最佳治疗策略提供了前所未有的机会。然而，这些复杂的数据也给统计分析带来了新的挑战。患者可能会不定期地去诊所就诊，可能会退出研究，可能会过早地停止开出的治疗。此外，可能存在“适应症混淆”，因为某些治疗可能优先开给病情较重的患者。这些功能可能会成为有效将丰富信息转化为有意义的知识的障碍。该项目的主要主题是开发新的数据分析方法，以应对这些重要的和反复出现的挑战。这项工作旨在通过开发新的方法来推动统计科学的发展，以应对这些困难的挑战，在这些挑战中，现有方法不适用或存在重大缺陷。这项研究还将为主题科学家提供一种在这些环境中处理科学问题的原则性方法，以发现患者的最佳治疗策略。本研究项目有以下几个目标。1)使用动态状态边际结构模型方法，建立生存分布作为治疗终止时间的函数的估计量。中断治疗在临床实践中经常出现，使分析和解释变得复杂。这里的目标是开发一个有启发性的演示，说明对这个问题的仔细概念化如何导致对合理治疗效果的明确定义，并得出有效的推论，形成处理治疗中断的原则方法。2)利用半参数理论，在信息截尾的情况下，从纵向观测研究中发展了结构嵌套均值模型(SNMM)的有效估计量。适应症时变混淆是一种普遍存在的现象，在治疗效果的评估中会造成选择偏差。人们已经提出了SNMM来克服这个问题；然而，它们在实践中的应用仍然不受欢迎，部分原因是估计器的效率高度依赖于估计方程的选择，而且在许多情况下该理论仍然不发达。研究人员计划开发在截尾存在的情况下改进SNMM中因果参数的估计器，与现有方法相比，该估计器获得了对滋扰模型规范的效率和稳健性。3)开发了一种新的连续时间SNMMS框架。在许多现实情况下，结果和治疗更有可能是在不规则间隔的时间点上测量的。现有的大多数SNMMS文献使用离散时间设置，这种设置过于简化，因此不切实际。该研究人员旨在为具有连续时间过程的SNMM提供一个统一的框架，建立一个因果推理的新研究领域。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Chronic diseases such as cardiovascular disease and HIV create an immense health and economic burden, both within the USA and globally. With recent technological advances, the chronic disease research enterprise is rapidly becoming data-intensive and data-driven. Massive and complex data provide unprecedented opportunities for discovering optimal treatment strategies for chronic diseases. However, these complex data also present novel challenges for statistical analysis. Patients may visit the clinic at irregular intervals, may drop out of studies, and may discontinue prescribed treatments prematurely. In addition, there may be "confounding by indication", in that some treatments may have been prescribed preferentially to sicker patients. These features can be barriers to effectively translating rich information into meaningful knowledge. The overarching theme of this project is to develop new data analysis methods that tackle these important and recurring challenges. This work aims to advance statistical science through the development of novel approaches to address these difficult challenges, where existing methods do not apply or suffer from major drawbacks. The research will also provide subject matter scientists with a principled way to approach scientific questions in these settings to discover optimal treatment strategies for patients. This research project has the following goals. 1) Develop estimators of survival distributions as a function of time to treatment discontinuation using a dynamic-regime marginal structural models approach. Treatment discontinuation arises frequently in clinical practice, complicating the analysis and interpretation. The objective here is to develop an instructive demonstration of how careful conceptualization of this problem leads to an unambiguous definition of a sensible treatment effect and to valid inferences, shaping a principled approach to dealing with treatment discontinuation. 2) Develop efficient estimators for Structural Nested Mean Models (SNMMs) from longitudinal observational studies in the presence of informative censoring using semiparametric theory. Time-varying confounding by indication is a widespread phenomenon and causes selection bias in the estimation of treatment effect. SNMMs have been proposed to overcome this issue; however, their use in practice is still unpopular, partly because the efficiency of the estimators is highly dependent on the choice of estimating equations, and the theory is still underdeveloped in many settings. The investigator plans to develop improved estimators of causal parameters in SNMMs in the presence of censoring, which gain both efficiency and robustness to nuisance model specification over existing methods. 3) Develop a new framework of continuous-time SNMMs. In many realistic situations, the outcomes and treatments are more likely to be measured at irregularly spaced time points. Most of the existing SNMMs literature uses a discrete-time setup, which is overly simplified and therefore impractical. The investigator aims to provide a unified framework for SNMMs with continuous-time processes, establishing a novel area of research in causal inference.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.