未经评估:开发感染性疾病诊断和治疗方法的研究通常需要在研究期间观察感染的发作。然而,当感染基础发生率较低时,测量效果所需的队列规模变大,招聘变得昂贵和延长。在一项COVID-19检测研究中,我们开发了一个模型,通过将招募目标定位于高风险个体来减少招募时间和资源。
UNASSIGNED:我们在美国各地的各个地点进行了观察性纵向队列研究,招募是在线健康和研究平台成员的成年人。通过与研究参与者的直接和纵向联系,我们应用机器学习技术从个人许可的社会经济和行为数据中计算个人风险评分,结合预测的当地患病率数据。然后,将建模的风险评分用于针对候选人进行一项假设的COVID-19检测研究。主要结果测量是根据风险模型与实际疫苗试验中的发病率进行比较的COVID-19的发病率。
UNASSIGNED:当我们使用66,040名参与者的风险评分招募一组平衡的参与者进行COVID-19检测研究时,与类似的真实世界研究队列相比,我们获得了4~7倍的COVID-19感染率.
UNASSIGNED:此风险模型提供了降低成本的可能性,提高分析的能力,并通过针对风险较高的招聘参与者来缩短研究时间。
UNASSIGNED: Studies for developing diagnostics and treatments for infectious diseases usually require observing the onset of infection during the
study period. However, when the infection base rate incidence is low, the cohort size required to measure an effect becomes large, and recruitment becomes costly and prolonged. We developed a model for reducing recruiting time and resources in a COVID-19 detection
study by targeting recruitment to high-risk individuals.
UNASSIGNED: We conducted an observational longitudinal cohort
study at individual sites throughout the U.S., enrolling adults who were members of an online health and research platform. Through direct and longitudinal connection with research participants, we applied machine learning techniques to compute individual risk scores from individually permissioned data about socioeconomic and behavioral data, in combination with predicted local prevalence data. The modeled risk scores were then used to target candidates for enrollment in a hypothetical COVID-19 detection
study. The main outcome measure was the incidence rate of COVID-19 according to the risk model compared with incidence rates in actual vaccine trials.
UNASSIGNED: When we used risk scores from 66,040 participants to recruit a balanced cohort of participants for a COVID-19 detection study, we obtained a 4- to 7-fold greater COVID-19 infection incidence rate compared with similar real-world
study cohorts.
UNASSIGNED: This risk model offers the possibility of reducing costs, increasing the power of analyses, and shortening study periods by targeting for recruitment participants at higher risk.