A Robust and Efficient Statistical Framework for Handling Missing-Not-At-Random Data in Patient Reported Outcomes and Beyond

患者报告结果(PRO)代表患者的健康状况，它直接来自患者，没有经过临床医生或其他任何人的解释，具有从患者的角度描述健康状况的独特功能；因此，PRO研究为知情的临床和政策决策以及提高医疗保健的质量和效率提供了巨大的希望。然而，PRO的质量和价值取决于许多因素，其中之一是缺失而不是随机(MNAR)问题。例如，患者可能因为抑郁程度而未能填写抑郁调查，或者病情较重的患者可能不太可能完成生活质量调查问卷。一般来说，由于患者的健康状况下降，这些专业人员会丢失，但由于没有观察到，下降的程度不得而知；因此，这些丢失的数据是信息性的，是Mnar。类似的情况也出现在大规模健康调查和电子健康记录数据库中。在这个项目中，私人投资促进机构将在PRO和其他类似情况下研究MANAR问题的统计方法和计算算法。该研究产品有可能应用于各种研究，如阿尔茨海默病、精神健康障碍、骨科和疼痛研究。PIs还将从事学科和跨学科层面的教育，受益者包括当地高中生和本科生、硕士和博士生以及生物医学研究人员。该项目还将为博士后学者提供研究机会。该项目的总体目标是建立一个开创性和转化性的统计方法框架，包括稳健的方法和有效的估计器，其中对缺失数据机制的假设被强加到最低水平，从而可以最大限度地灵活地应用所开发的方法。基于没有足够的方法来测试缺失数据机制的正确性这一公认的事实，PI将采用影子变量方法来实现模型识别，并且基本上不对该机制做出进一步的假设，从而为模型误指定提供最大可能的保护。通过利用基于模型的似然及其关联的半参数结构，该方法对机制模型的错误指定具有健壮性。在这个项目中开发的统计方法将被应用到高效的R包和用户友好的界面中，供研究人员使用，他们的主要目标是分析缺失的数据，特别是Mnar数据。该奖项反映了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。

The patient-reported outcome (PRO), representing the status of the patient's health that comes directly from the patient without interpretation by the clinician or anyone else, has the unique feature of describing health status from the viewpoint of the patient; therefore, the PRO research holds great promise for informed clinical and policy decision-making, as well as for improving the quality and efficiency of healthcare. However, the quality and value of PRO is contingent on a number of factors, and one of them is the missing-not-at-random (MNAR) issue. For instance, patients might fail to fill in a depression survey because of their level of depression, or patients who are sicker may be less likely to complete a quality-of-life questionnaire. In general, these PROs are missing due to the patient's declining health status, but the extent of decline is not known because it is not observed; hence, these missing data are informative and are MNAR. Similar situations also appear in large-scale health surveys and electronic health records database. In this project, the PIs will study statistical methodology and computational algorithm for the MNAR issue in PRO as well as in other similar situations. The research product has the potential to be applied to various studies, such as Alzheimer's disease, mental health disorders, orthopedics, and pain research. The PIs will also engage in education at both disciplinary and interdisciplinary levels, with beneficiaries ranging from local high school students and undergraduates, to master and PhD students, and to biomedical investigators. The project will also provide research opportunities for postdoctoral scholars. The overarching goal of this project is to establish a groundbreaking and translational statistical methodology framework including robust methods as well as efficient estimators, where the assumption on the missing data mechanism is imposed at a minimum level hence the developed methods can be applied with the largest flexibility. Motivated by the well-recognized fact that there is no adequate way to test the correctness of the missing data mechanism, the PIs will adopt the shadow variable approach to achieve the model identification and essentially make no further assumptions on the mechanism, thereby provide largest possible protection to model misspecification. The methodology is robust against the mechanism model misspecification by leveraging the model-based likelihood and its associated semiparametric structure. The statistical methods developed in this project will be implemented into efficient R packages and user-friendly interfaces for researchers whose primary goal is the analysis of missing data, especially MNAR data.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.