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.

项目摘要/摘要当前建议的主要目的是促进透明和可再现的知识存储库，用于循证医学。医疗保健的财富电子健康记录中已经可以使用的数据可以更好地帮助指导从随机对照试验中的治疗选择和补充发现。这个建议解决两个主要障碍。首先是从中获得高质量证据的挑战在存在偏见和混杂因素的情况下，尤其是临时数据的观察数据。第二个是患者隐私和其他问题阻止披露来源数据，这是阻碍可重复的研究 - 目前有大量的医学文献发现指导临床实践，但不能独立审查。我们将解决这些通过创新方法，局部控制挑战，既纠正偏见，又可以披露特定问题的微聚集数据以复制研究结果没有披露个人信息。本地控制背后的关键思想是形成许多均质的患者簇可以在统计上比较替代治疗校正测量的偏见和混杂因素，类似于随机块设计。我们的方法论提供了一个统一的框架，以实现开放，高质量，比较通过组合新型特征选择方法，基于分数，有效研究阶乘实验设计，随着生存分析的进步，包括竞争风险。我们将创建一个包含用于非参数偏见校正的方法家族的公共r软件包以及横断面，病例对照和生存分析设置中的统计披露控制。这项研究的成功也将使一个新颖的模型，我们将“包裹数据共享”称为促进针对特定问题的专有数据源的开放选择性发布 - 同样，保护患者隐私，专有利益和公共利益。我们的研究将促进由国家和国家支持的基于证据的医学的目标全球知识基础是100的数千个比较有效性问题数百万患者的健康记录。该应用程序通过协助来支持NLM任务通过传播和交换医学和相关科学的发展医学和健康进步的重要信息。具体目的是（1）开发和评估一种基于生存的局部控制方法，用于偏见校正治疗比较事件时间观察数据；（2）开发和评估当地控制 - 基于可重复研究的微聚集。