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和全球范围内造成了巨大的健康和经济负担。 随着最近的技术进步，慢性病研究企业正在迅速成为数据密集型和数据驱动型。 大量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。 患者可能不定期到诊所就诊，可能退出研究，也可能提前停止处方治疗。此外，可能存在“适应症混淆”，因为某些治疗可能优先为病情较重的患者开出。 这些特征可能是有效地将丰富的信息转化为有意义的知识的障碍。该项目的首要主题是开发新的数据分析方法，以应对这些重要和反复出现的挑战。这项工作旨在通过开发新的方法来推进统计科学，以应对现有方法不适用或存在重大缺陷的困难挑战。该研究还将为主题科学家提供一种原则性的方法来解决这些环境中的科学问题，以发现患者的最佳治疗策略。 该研究项目有以下目标。1)使用动态方案边际结构模型方法，开发生存分布的估计值作为治疗中止时间的函数。在临床实践中经常出现治疗中止，使分析和解释复杂化。 这里的目的是开发一个有启发性的示范，如何仔细概念化这个问题导致一个明确的定义，一个合理的治疗效果和有效的推论，形成一个原则性的方法来处理治疗中止。 2)利用半参数理论，在信息删失存在的情况下，从纵向观察研究中为结构嵌套均值模型（SNESTs）开发有效的估计量。适应症的时变混杂是一种普遍现象，并导致治疗效果估计的选择偏倚。 SNARCHES已被提出来克服这个问题，但是，他们在实践中的使用仍然是不受欢迎的，部分原因是估计的效率是高度依赖于估计方程的选择，和理论仍然是欠发达的许多设置。 研究者计划在删失的情况下，开发SNEARTH中因果参数的改进估计量，该估计量在现有方法的基础上获得效率和对滋扰模型规范的鲁棒性。 3)提出了一种新的连续时间SNANTRONIC框架。在许多现实情况下，结果和治疗更有可能在不规则的时间点进行测量。大多数现有的SNARCHIP文献使用离散时间设置，这是过于简化，因此不切实际。该研究员旨在为具有连续时间过程的SNEARLY提供一个统一的框架，建立一个新的因果推理研究领域。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值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.