Theory and Methods for Causal Inference in Chronic Diseases

慢性病因果推断的理论与方法

• 批准号: 1811245
• 负责人: Shu Yang
• 金额: $ 12万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-15 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1811245&HistoricalAwards=false
• 关键词: Theory; Methods; Causal; Inference; Chronic

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.

诸如心血管疾病和艾滋病毒之类的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。 随着技术的最新进展，慢性病研究企业正在迅速变得密集型和数据驱动。 大量和复杂的数据为发现慢性疾病的最佳治疗策略提供了前所未有的机会。但是，这些复杂的数据还带来了统计分析的新挑战。 患者可能会以不规则的间隔去诊所，可能会退出研究，并可能过早地处方治疗。此外，可能会出现“通过指示混淆”，因为某些治疗可能已针对病人的患者开具。 这些功能可能是有效地将丰富信息转化为有意义的知识的障碍。该项目的总体主题是开发新的数据分析方法，以应对这些重要且经常出现的挑战。这项工作旨在通过开发新的方法来解决这些困难挑战，而现有方法不适用或遭受重大缺点。这项研究还将为主题科学家提供一种原则性的方法，以在这些情况下解决科学问题，以发现患者的最佳治疗策略。 该研究项目具有以下目标。 1）开发生存分布的估计量，是使用动态优势边缘结构模型方法进行治疗停用时间的函数。在临床实践中经常出现治疗停止，使分析和解释变得复杂。 这里的目的是建立一个有启发性的证明，以表明该问题的仔细概念化如何导致对明智的治疗效果的明确定义和有效的推断，从而塑造了处理终止治疗的有原则的方法。 2）在使用半参数理论进行信息审查的情况下，从纵向观察性研究中开发了有效的估计量。随时间变化的混杂是一种广泛的现象，在估计治疗效果时会导致选择偏见。 已经提出了SNMM来克服这个问题。但是，它们在实践中的使用仍然不受欢迎，部分原因是估计器的效率高度取决于估计方程的选择，并且在许多情况下，该理论仍然不发达。 研究人员计划在审查的存在下开发SNMM中因果参数的估计值的改进，这在现有方法上既可以提高效率和鲁棒性，从而提高效率和鲁棒性。 3）开发一个连续时间SNMM的新框架。在许多现实情况下，结果和治疗更可能在不规则间隔时间点测量。大多数现有的SNMMS文献都使用离散的时间设置，该设置过于简化，因此不切实际。研究人员的目的是通过连续时间流程为SNMM提供一个统一的框架，建立了因果推理的新型研究领域。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子评估和更广泛影响的评论来获得支持的标准。

项目成果

Flexible Imputation of Missing Data, 2nd ed.: Boca Raton, FL: Chapman & Hall/CRC Press, 2018, xxvii + 415 pp., $91.95(H), ISBN: 978-1-13-858831-8.

缺失数据的灵活插补，第二版：博卡拉顿，佛罗里达州：查普曼

• DOI: 10.1080/01621459.2019.1662249
• 发表时间: 2019
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7
• 作者: Yang, Shu

Utilizing stratified generalized propensity score matching to approximate blocked trial designs with multiple treatment levels

利用分层广义倾向评分匹配来近似具有多个治疗水平的封闭试验设计

• DOI: 10.1080/10543406.2.22.2065507
• 发表时间: 2022
• 期刊: Journal of biopharmaceutical statistics
• 影响因子: 1.1
• 作者: Corder, Nathan;Yang, Shu
• 通讯作者: Yang, Shu

Integration of data from probability surveys and big found data for finite population inference using mass imputation

• 发表时间: 2021
• 作者: Jae Kwang Kim;Y. Hwang;Paul H. Chook;Shu Yang

Robust estimation for moment condition models with data missing not at random

• DOI: 10.1016/j.jspi.2020.01.001
• 发表时间: 2020-07
• 期刊: Journal of Statistical Planning and Inference
• 影响因子: 0.9
• 作者: Wei Li;Shu Yang;Peisong Han

Causal inference with confounders missing not at random

• DOI: 10.1093/biomet/asz048
• 发表时间: 2017-02
• 期刊: Biometrika
• 影响因子: 2.7
• 作者: Shu Yang;Linbo Wang;Peng Ding