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

项目概要/摘要 在COVID-19大流行期间，迫切需要高质量的研究数据， 支持患者护理，预测结果，识别和评估治疗，分配资源，并使 业务和政策决定。虽然前瞻性研究产生更高质量的证据，但回顾性研究 重复使用临床数据的研究可以在更短的时间内以更低的成本执行，这两者都是 对研究流感大流行至关重要。不幸的是，已经表明， 可用的COVID-19数据受到各种形式的选择偏差和信息偏差的限制，这些偏差可能 导致研究和分析中的无效结果以及由此产生的医疗实践中的差异。 这项工作的目的是研究临床上存在的选择和信息偏差， 通过整合来自OHSU和国家COVID队列的COVID-19数据集得出COVID-19数据集 与临床，流行病学，社交媒体和公民产生的新的和传统的来源合作 数据从每个数据源中，我们将提取表明COVID-19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问题。以检测是否存在 选择偏差，我们将构建和比较每个社会决定因素的分类概率分布 在每个数据源中的COVID-19病例。这些分布的差异将表明选择偏倚， 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， COVID-19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的因素而异。 因此，这一建议解决了知识方面的一个重大差距：不仅要了解 谁受到COVID-19的影响，但我们可用于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 COVID-19数据中的偏见和信息偏见可以指导统计和分析方法的使用， 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COVID-19的自然病程，并识别处于风险中的患者 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.