Identifying and understanding drivers of selection bias and information bias in clinical COVID-19 data
识别和理解临床 COVID-19 数据中选择偏差和信息偏差的驱动因素
基本信息
- 批准号:10380032
- 负责人:
- 金额:$ 16.75万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-04-01 至 2024-03-31
- 项目状态:已结题
- 来源:
- 关键词:AcademyAddressAffectAgeAmericanAutomobile DrivingBehavioralCOVID-19COVID-19 impactCOVID-19 pandemicCOVID-19 patientCOVID-19 surveillanceCaringCategoriesCenters for Disease Control and Prevention (U.S.)Cessation of lifeClinicalClinical DataClinical ResearchCommunity SurveysCountryDataData CollectionData SetData SourcesDecision MakingDiseaseElectronic Health RecordEpidemiologyEthnic OriginEvaluationGeographyGoalsHealthHealth SciencesHealth Services AccessibilityHealthcareIndividualKnowledgeLeadLearningManualsMeasuresMediatingMedicalMedicineOregonPatient CarePatientsPoliciesPopulationPrevalenceProbabilityRaceResearchResource AllocationResourcesRetrospective StudiesRiskRoleSamplingSelection BiasSourceStructureTest ResultTestingTimeUniversitiesWorkauthoritycare seekingcohortcoronavirus diseasecostdata repositorydata reuseepidemiology studyhealth care availabilityhealth care disparityhealth care service utilizationhealth dataimprovedmembermultiple data sourcesnoveloperationoutcome predictionpandemic diseasepopulation healthprospectivesexsocialsocial determinantssocial factorssocial health determinantssocial influencesocial mediasurveillance datatherapy developmenttrustworthiness
项目摘要
Project Summary / Abstract
During the COVID-19 pandemic, there is an immediate need for high-quality data for studies that
support patient care, predict outcomes, identify and evaluate treatments, allocate resources, and make
operations and policy decisions. While prospective research produces higher-quality evidence, retrospective
studies that reuse clinical data can be executed in a shorter time frame and for less cost, both of which are
crucial for research in a pandemic. Unfortunately, it has been shown that the usefulness and validity of
available COVID-19 data are constrained by various forms of selection bias and information bias, which may
lead to non-valid findings in research and analytics and disparities in resulting healthcare practices.
The objective of the proposed work is to study the selection and information biases present in clinically
derived COVID-19 datasets by integrating COVID-19 datasets from OHSU and the National COVID Cohort
Collaborative with novel and traditional sources of clinical, epidemiological, social media, and citizen-generated
data. From each data source we will extract data indicating COVID-19, as well as a set of social determinants
of health that are commonly associated with healthcare utilization and access. To test for the presence of
selection bias, we will construct and compare categorical probability distributions for each social determinant
across COVID-19 cases in each data source. Differences in these distributions will indicate selection bias in
one or more of the data sources. Next we will determine information bias by extending and adapting tests for
missingness and other forms of information bias in the COVID-19 datasets to determine if the quantity and
quality of these data vary with respect to clinical factors and those related to social determinants of health.
This proposal therefore addresses a significant gap in knowledge: understanding not just the disparities
in who is impacted by COVID-19, but who is represented by the data we have available for learning more
about the disease. The identification and estimation the influence of social determinants of health on selection
bias and information bias in COVID-19 data can guide the use of statistical and analytic approaches that can
improve the external and internal validity of research and analytics that rely on these data, including estimates
of disease prevalence, understanding the natural course of COVID-19, and identifying patients who are at risk
for severe disease.
项目摘要/摘要
在新冠肺炎大流行期间,迫切需要高质量的数据来进行以下研究
支持患者护理,预测结果,识别和评估治疗,分配资源,并做出
运营和政策决策。虽然前瞻性研究产生了更高质量的证据,但回溯性研究
重复使用临床数据的研究可以在更短的时间框架内以更低的成本进行,这两者都是
对大流行中的研究至关重要。不幸的是,事实证明,这种方法的有用性和有效性
现有的新冠肺炎数据受到各种形式的选择偏差和信息偏差的制约,这可能
导致研究和分析中的无效发现,以及由此产生的医疗实践中的差异。
这项拟议工作的目标是研究临床上存在的选择和信息偏差。
通过整合来自卫生与公众服务部和国家冠状病毒队列的新冠肺炎数据集来获得新冠肺炎数据集
与新的和传统的临床、流行病学、社交媒体和公民来源合作
数据。我们将从每个数据源提取指示新冠肺炎的数据,以及一组社会决定因素
通常与医疗保健利用和获取相关的健康状况。测试…的存在
选择偏差,我们将构建并比较每个社会决定因素的绝对概率分布
每个数据源中的新冠肺炎案例。这些分布的差异将表明在
一个或多个数据源。接下来,我们将通过扩展和调整测试来确定信息偏差
新冠肺炎数据集中的缺失和其他形式的信息偏差,以确定数量和
这些数据的质量因临床因素和与健康的社会决定因素有关而有所不同。
因此,这一建议解决了知识上的一个重大差距:理解的不仅仅是差异
受新冠肺炎影响的人,但我们可以用来了解更多信息的数据代表了谁
关于这种疾病。健康的社会决定因素对选择的影响的识别和估计
新冠肺炎数据中的偏差和信息偏差可以指导使用统计和分析方法,这些方法可以
提高依赖这些数据的研究和分析的外部和内部有效性,包括估计
了解疾病患病率,了解新冠肺炎的自然病程,并识别有风险的患者
对于严重的疾病。
项目成果
期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Healthcare utilization is a collider: an introduction to collider bias in EHR data reuse.
- DOI:10.1093/jamia/ocad013
- 发表时间:2023-04-19
- 期刊:
- 影响因子:6.4
- 作者:Weiskopf, Nicole G.;Dorr, David A.;Jackson, Christie;Lehmann, Harold P.;Thompson, Caroline A.
- 通讯作者:Thompson, Caroline A.
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Nicole Gray Weiskopf的其他文献
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{{ truncateString('Nicole Gray Weiskopf', 18)}}的其他基金
Health equity and the impacts of EHR data bias associated with social determinants
健康公平以及与社会决定因素相关的电子病历数据偏差的影响
- 批准号:
10584190 - 财政年份:2023
- 资助金额:
$ 16.75万 - 项目类别:
Identifying and understanding drivers of selection bias and information bias in clinical COVID-19 data
识别和理解临床 COVID-19 数据中选择偏差和信息偏差的驱动因素
- 批准号:
10192372 - 财政年份:2021
- 资助金额:
$ 16.75万 - 项目类别:
Operationalizing Machine Learning and Discrete Event Simulation Models to Improve Clinic Efficiency
运用机器学习和离散事件模拟模型来提高诊所效率
- 批准号:
10460170 - 财政年份:2020
- 资助金额:
$ 16.75万 - 项目类别:
Operationalizing Machine Learning and Discrete Event Simulation Models to Improve Clinic Efficiency
运用机器学习和离散事件模拟模型来提高诊所效率
- 批准号:
10664923 - 财政年份:2020
- 资助金额:
$ 16.75万 - 项目类别:
Measuring and improving data quality for clinical quality measure reliability
测量和提高临床质量测量可靠性的数据质量
- 批准号:
9761576 - 财政年份:2017
- 资助金额:
$ 16.75万 - 项目类别:
Measuring and improving data quality for clinical quality measure reliability
测量和提高临床质量测量可靠性的数据质量
- 批准号:
9428949 - 财政年份:2017
- 资助金额:
$ 16.75万 - 项目类别:
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