权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Improving confounder control in EHR-based studies of cancer epidemiology

改善基于 EHR 的癌症流行病学研究中的混杂因素控制

基本信息

批准号：
9894108
负责人：
Rebecca Hubbard
金额：
$ 1.88万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-01-01 至 2020-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9894108
关键词：
Accounting Address Characteristics Clinical Code Colon Carcinoma Comorbidity Computer software Data Data Collection Data Element Diabetes Mellitus Diagnosis Disease Electronic Health Record Enrollment Epidemiologic Methods Family Cancer History Gold Health Manuals Measurement Measures Medical Records Methodological Studies Methodology Methods Observational Study Outcome Patients Pattern R programming language Recurrence Research Research Design Research Methodology Residual state Risk Smoking Smoking History Smoking Status Source Statistical Methods Validation Validity of Results Washington base cancer epidemiology cancer recurrence cohort colon cancer patients cost electronic data improved indexing innovation interest neoplasm registry non-smoker novel novel strategies population based secondary analysis tool validation studies web site

项目摘要

! Project Summary Data from Electronic Health Records (EHR) are a valuable research tool, providing information on outcomes and exposures that would be costly and difficult to obtain through primary data collection. However, EHR data capture is driven by clinical and administrative rather than research needs, necessitating substantial methodological innovation to obtain valid results. While a number of prior methodological studies have focused on reducing confounding in observational studies conducted using EHR data, they have not considered the risk of residual confounding that results when confounder variables are measured with error. The proposed study will develop novel statistical tools tailored to the EHR context to address measurement error and missing data in confounders. Under Aim 1 we will use a recently developed statistical approach, integrated likelihood, to develop a method for confounder control using imperfect confounders that does not require validation data. Under Aim 2, we will develop an index of sensitivity of study results to the assumption of “informative presence,” i.e. that absence of information on a confounder is indicative of absence of the confounder. Novel methods will be evaluated and compared to standard approaches using simulated data and applied to existing data from a study of colon cancer recurrence. Statistical software code for these methods will be developed in the R programming language and disseminated via our project website and Github. This research will provide methodological tools to improve the validity of results obtained through secondary analysis of EHR-derived data. !

! 项目摘要来自电子健康记录（EHR）的数据是一种有价值的研究工具，可提供有关结果的信息以及通过原始数据收集获得的成本高昂且难以获得的风险。然而，EHR数据捕获是由临床和管理而不是研究需求驱动的，需要大量的方法创新，取得实效。虽然以前的一些方法研究集中在在使用EHR数据进行的观察性研究中，他们没有考虑到在测量混杂变量时产生误差的残余混杂。拟定研究将开发针对EHR环境的新型统计工具，以解决测量误差和数据缺失问题混杂因素。在目标1下，我们将使用最近开发的统计方法，即综合似然法，开发一种使用不完全混杂因素的混杂因素控制方法，不需要验证数据。在目标2之下，我们将制定一个指标，衡量研究结果对“信息量”假设的敏感性存在”，即缺乏关于混杂因素的信息表明不存在混杂因素。小说方法将进行评估，并与标准方法进行比较，使用模拟数据，并应用于现有的结肠癌复发研究的数据。这些方法的统计软件代码将在 R编程语言，并通过我们的项目网站和Github传播。这项研究将提供方法工具，以提高通过二次分析EHR派生的结果的有效性数据 !