Unified Approaches for Missing Data in Observational Studies

描述（由申请人提供）：现有的大型医疗保健数据库，如健康保险索赔、医疗补助和医疗保险索赔、国家和地区癌症登记处以及电子病历，为包括癌症监测在内的各个医疗领域的比较研究提供了机遇，同时也带来了挑战。由于这些数据库的“观察性”，存在混淆偏倚。此外，由于这些数据库是为非研究目的收集的，因此通常会出现数据缺失。每个问题都得到了广泛的研究。但目前还缺乏统一处理这两个问题的分析方法。迫切需要发展新的统计方法和软件工具，以弥合现有观测数据库与相对有效性知识需求之间的差距。具体目的：我们建议发展两种方法来分析不完整的观测数据。这两种方法都是现有方法的新应用。第一种，多重鲁棒方法，将基于因果推理和缺失数据模型的双重鲁棒理论发展。第二种方法是基于树的插值方法，它将多重插值方法与基于树的数据自适应回归技术相结合，以实现鲁棒推理。我们将通过广泛的模拟研究来评估和比较新分析方法的性能。我们还将把这些方法应用于一项现有的乳腺癌依从性研究，比较两种辅助激素疗法对药物依从率的影响。此外，我们建议开发和记录软件程序，以促进所提出的方法的实施。研究设计：新方法将首先在仅包含缺失混杂因素的简单设置中开发，然后扩展到包含缺失混杂因素和缺失结果的更一般设置。在我们的方法开发过程中，我们假设数据是随机丢失的。我们将考虑在使用现有医疗保健数据库的比较研究中经常观察到的各种缺失数据模式。影响：该项目的潜在影响是显著的，因为所提议的研究的成功实施将导致新的分析方法和软件工具，以帮助研究人员正确有效地分析现有的观测数据库中缺失的数据，以获得有效的比较有效性和安全性结果。随着建立全国性电子医疗记录系统的不断努力，分析这些二级数据库的结果将有助于解决许多重要的公共卫生和医学问题，这些问题要么由于伦理和实际原因无法通过随机临床试验（rct）解决，要么需要更多的时间和资源。

DESCRIPTION (provided by applicant): Large existing healthcare databases, e.g., health insurance claims, Medicaid and Medicare claims, national and regional cancer registries, and electronic medical records present not only opportunities but also challenges for comparative research in various medical areas including cancer surveillance. Confounding bias exists due to the "observational" nature of these databases. Moreover, missing data commonly occur as these databases are collected for non-research purposes. Each problem has been extensively studied. But analytic approaches that tackle both issues in a unified manner are lacking. There is a critical need to develop novel statistical methods as well as software tools to bridge the gap between existing observational databases and needs in the knowledge of comparative effectiveness. Specific Aims: We propose to develop two methods to analyze incomplete observational data. Both methods are novel applications of existing methods. The first one, multiply-robust method, will be developed based on the doubly-robust theory for causal inference and missing data models. The second one, tree-based imputation method, will integrate the multiple imputation approach with the tree-based, data-adaptive regression techniques for robust inference. We will evaluate and compare the performance of the new analytic methods via extensive simulation studies. We will also apply the methods to an existing breast cancer adherence study to compare the effect between two adjuvant hormone therapies on medication adherence rate. In addition, we propose to develop and document software programs to facilitate implementation of the proposed methods. Research Design: The new methods will be firstly developed in simple settings with missing confounders only and then be extended to more general settings with both missing confounders and missing outcomes. Throughout our methods development, we assume data are missing at random. We will consider various missing data patterns that are commonly observed in comparative studies using existing healthcare databases. Impact: The potential impact of this project is significant because the successful implementation of the proposed research will result in novel analytic methods as well as software tools to help investigators correctly and efficiently analyze existing observational databases with missing data to obtain valid comparative effectiveness and safety results. With the ongoing efforts in building nationwide electronic medical records systems, the results from analyzing these secondary databases will help address many important public health and medical questions that either, due to ethical and practical reasons, cannot be addressed by randomized clinical trials (RCTs), or require much more time and resources to address via RCTs.