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，例如健康保险索赔、医疗补助和医疗保险索赔、国家和地区癌症登记以及电子病历，不仅为包括癌症监测在内的各种医学领域的比较研究提供了机会，也提出了挑战。由于这些数据库的“观察性”性质，存在混淆的偏见。此外，由于这些数据库是为非研究目的而收集的，因此通常会出现数据丢失的情况。每个问题都得到了广泛的研究。但缺乏统一解决这两个问题的分析方法。迫切需要开发新的统计方法和软件工具，以弥补现有观测数据库与相对有效性知识需求之间的差距。具体目标：我们建议开发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。第一种是基于因果推理和缺失数据模型的双稳健理论的乘稳健方法。第二种是基于树的推算方法，它将把多重推算方法与基于树的数据自适应回归技术相结合，以进行稳健的推理。我们将通过广泛的模拟研究来评估和比较新的分析方法的性能。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，以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，以促进拟议方法的实施。研究设计：新方法将首先在仅缺少混杂因素的简单环境中开发，然后扩展到既缺少混杂因素又缺少结果的更一般情况。在我们的方法开发过程中，我们假设数据随机丢失。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重大的，因为拟议研究的成功实施将产生新的分析方法以及软件工具，以帮助调查人员正确和有效地分析存在缺失数据的现有观测数据库，以获得有效的比较有效性和安全性结果。随着在全国范围内建立电子病历系统的不断努力，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践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通过随机临床试验(RCT)解决。

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.