Tong Jiayi, Sun Yifei, Hubbard Rebecca A, Saine M Elle, Xu Hua, Zuo Xu, Lin Lifeng, Weng Chunhua, Schmid Christopher H, 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 19104, United States.
Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA 19104, United States.
J Am Med Inform Assoc. 2025 Sep 3. doi: 10.1093/jamia/ocaf111.
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 2-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 2-stage procedure validated with 2 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.