Identifying and understanding drivers of selection bias and information bias in clinical COVID-19 data

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。

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.