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Unified Approaches for Missing Data in Observational Studies

观察研究中缺失数据的统一方法

基本信息

批准号：
8332773
负责人：
Lingling Li
金额：
$ 21.02万
依托单位：
HARVARD PILGRIM HEALTH CARE, INC.
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-09-14 至 2014-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8332773
关键词：
Address Adherence Adjuvant Area Clinical Clinical Trials Comparative Study Computer software Computerized Medical Record Conscious Data Data Sources Databases Diagnostic Neoplasm Staging Ethics Failure Goals Guidelines Health Insurance Health Status Healthcare Knowledge Laboratories Lead Malignant Neoplasms Medicaid Medical Medical Research Medicare claim Methods Monitor Nature Observational Study Office Visits Outcome Participant Patients Pattern Performance Physicians Population Public Health Randomized Clinical Trials Regional Cancer Registries Research Research Design Research Personnel Resources Safety Software Tools Statistical Methods System Techniques Test Result Time Trees base clinical practice comparative comparative effectiveness data modeling demographics effectiveness research follow-up hormone therapy interest malignant breast neoplasm medication compliance method development neoplasm registry novel programs simulation theories tool

项目摘要

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.

描述（由申请人提供）：大型现有医疗保健数据库，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和电子医疗记录不仅为包括癌症监测在内的各种医疗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”性质，存在混杂偏倚。此外，由于这些数据库是为非研究目的收集的，因此经常会出现数据缺失。每个问题都得到了广泛的研究。但缺乏以统一的方式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对相对有效性知识的需求之间的差距。具体目标：我们提出了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。第一种方法是基于双重稳健理论的多重稳健方法，它适用于因果推理和缺失数据模型。第二种是基于树的插补方法，将多重插补方法与基于树的数据自适应回归技术相结合，以实现稳健的推断。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。我们还将应用现有的乳腺癌依从性研究的方法，比较两种辅助激素治疗对药物依从率的影响。此外，我们建议开发和文档的软件程序，以促进所提出的方法的实施。研究设计：新方法将首先在仅缺失混杂因素的简单环境中开发，然后扩展到缺失混杂因素和缺失结局的更一般环境。在我们的方法开发过程中，我们假设数据是随机缺失的。我们将考虑在使用现有医疗保健数据库的比较研究中常见的各种缺失数据模式。影响：该项目的潜在影响是显著的，因为拟议研究的成功实施将产生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察数据库，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病历系统的持续努力，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，这些问题由于伦理和实际原因无法通过随机临床试验（RCT）解决，或者需要更多的时间和资源通过RCT解决。