A microaggregation framework for reproducible research with observational data: addressing biases while protecting personal identities

利用观察数据进行可重复研究的微聚合框架：在保护个人身份的同时解决偏见

基本信息

批准号：
9306948
负责人：
Christophe G. Lambert
金额：
$ 16.29万
依托单位：
UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-07-01 至 2019-06-30
项目状态：
已结题

项目摘要

PROJECT SUMMARY/ABSTRACT The primary objective of the current proposal is to foster efforts towards transparent and reproducible knowledge repositories for evidence-based medicine. The wealth of healthcare data already available in electronic health records could be better utilized to help guide treatment choices and compliment findings from randomized controlled trials. This proposal addresses two major obstacles. The first is the challenge of deriving high-quality evidence from observational data in the presence of biases and confounders, particularly with temporal data. The second is that patient privacy and other concerns prevent disclosure of source data, which hinders reproducible research -- currently there is a vast body of medical literature whose findings guide clinical practice, yet cannot be independently scrutinized. We will address these challenges through an innovative methodology, local control, which both corrects biases and enables disclosure of question-specific microaggregated data to reproduce research findings without disclosure of individual information. The key idea behind local control is to form many homogeneous patient clusters within which one can compare alternate treatments, statistically correcting for measured biases and confounders, analogous to a randomized block design. Our methodology provides a unified framework for enabling open, high quality, comparative effectiveness research by combining novel feature selection approaches, based on fractional factorial experimental design, with advances in survival analysis, including competing risks. We will create a public R package containing a family of methods for nonparametric bias correction and statistical disclosure control in cross-sectional, case-control, and survival analysis settings. Success of this research will also enable a novel model, we term “parcelled data sharing” to facilitate open selective release of proprietary data sources for specific questions -- simultaneously protecting patient privacy, proprietary interests, and the public good. Our research will contribute to the goal of evidence-based medicine being supported by national and global knowledge bases on thousands of comparative effectiveness questions from 100’s of millions of patients’ health records. This application supports the NLM mission by assisting in the advancement of medical and related sciences through the dissemination and exchange of important information to the progress of medicine and health. The specific aims are to (1) Develop and evaluate a survival-based local control methodology for bias-corrected treatment comparisons in time-to-event observational data; and (2) Develop and evaluate local control- based microaggregation for reproducible research.

项目概要/摘要当前提案的主要目标是促进努力实现透明和循证医学的可重复知识库。医疗保健的财富。可以更好地利用电子健康记录中现有的数据来帮助指导治疗选择和随机对照试验的结果值得称赞。解决了两个主要障碍，第一个是从中获取高质量证据的挑战。存在偏差和混杂因素的观察数据，特别是时间数据。第二个是患者隐私和其他问题阻止了源数据的泄露，这阻碍了可重复的研究——目前有大量的医学文献研究结果指导临床实践，但无法进行独立审查，我们将解决这些问题。通过创新方法、本地控制来应对挑战，既纠正偏见，又能够披露特定问题的微聚合数据以重现研究结果不披露个人信息的情况下，地方控制背后的关键思想是形成许多。同质的患者群体，可以在其中比较替代治疗方法，纠正测量的偏差和混杂因素，类似于我们的随机区组设计。方法论提供了一个统一的框架，以实现开放、高质量、可比较的基于分数的结合新颖特征选择方法的有效性研究因子实验设计，生存分析的进步，包括竞争风险。将创建一个公共 R 包，其中包含一系列用于非参数偏差校正的方法横截面、病例对照和生存分析环境中的统计披露控制。这项研究的成功还将实现一种新颖的模型，我们称之为“分段数据共享” 促进针对特定问题公开选择性地发布数据源—— 同时保护患者隐私、专有利益和公共利益。研究将有助于实现国家和地区支持的循证医学目标全球知识库基于来自数百个国家的数千个比较有效性问题该应用程序通过协助支持 NLM 的使命。通过传播和交流医学和相关科学的进步医学和健康进步的重要信息，具体目标是 (1) 开发和评估基于生存的局部控制方法以进行偏差校正治疗事件发生时间观测数据的比较；以及 (2) 制定和评估本地控制基于微聚集的可重复研究。