Tong Jiayi, Sun Yifei, Hubbard Rebecca A, Saine M Elle, Xu Hua, Zuo Xu, Lin Lifeng, Weng Chunhua, Schmid Christopher, Kimmel Stephen E, Umscheid Craig A, Cuker Adam, Chen Yong
The Center for Health AI and Synthesis of Evidence (CHASE), Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, USA.
Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, USA.
medRxiv. 2025 Jul 16:2025.07.15.25331581. doi: 10.1101/2025.07.15.25331581.
By October 1, 2024, over 450,000 COVID-19 manuscripts were published, with 10% posted as unreviewed preprints. While they accelerate knowledge sharing, their inconsistent quality complicates systematic studies.
We propose a two-stage method to include preprints in meta-analyses. In Stage A, preprints are integrated through restriction or imputation and weighted by a confidence score reflecting their publication likelihood. In Stage B, we assess and adjust for potential publication or reporting biases.
This preliminary study employed a two-stage procedure validated with two COVID-19 treatment case studies. For hydroxychloroquine, the relative risk (RR) was 1.06 [95% CI: 0.62, 1.80], suggesting no mortality benefit over placebo. For corticosteroids, the RR was 0.88 [95% CI: 0.62, 1.27], which, while not statistically significant, aligns with evidence supporting a mortality benefit.
Our research aims to bridge a significant methodological gap by providing a solution for timely evidence synthesis, particularly in the face of the overwhelming number of publications surrounding COVID-19.
This preliminary study presents a method to efficiently synthesize COVID-19 research, including non-peer-reviewed preprints, to support clinical and policy decisions amidst the information surge.
到2024年10月1日,已发表了超过45万篇关于新冠病毒病的手稿,其中10%是以未经评审的预印本形式发布的。虽然它们加快了知识共享,但质量参差不齐使系统性研究变得复杂。
我们提出一种两阶段方法,将预印本纳入荟萃分析。在A阶段,通过限制或插补整合预印本,并根据反映其发表可能性的置信度得分进行加权。在B阶段,我们评估并调整潜在的发表或报告偏倚。
这项初步研究采用了一个两阶段程序,并通过两个新冠病毒病治疗案例研究进行了验证。对于羟氯喹,相对风险(RR)为1.06 [95%置信区间:0.62, 1.80],表明与安慰剂相比无死亡率获益。对于皮质类固醇,RR为0.88 [95%置信区间:0.62, 1.27],虽然无统计学显著性,但与支持死亡率获益的证据一致。
我们的研究旨在通过提供一种及时进行证据综合的解决方案来弥合一个重大的方法学差距,特别是面对围绕新冠病毒病的大量出版物。
这项初步研究提出了一种方法,可有效综合新冠病毒病研究,包括未经同行评审的预印本,以便在信息激增的情况下支持临床和政策决策。