Each week, we select a recently published Open Access article to feature. This week’s article comes from Statistics in Medicine and proposes a method for combining patient-level data from multiple randomized clinical trials especially in the pursuit of rapid results on studies related to the treatment of COVID-19.
The article’s abstract is given below, with the full article available to read here.
Prospective individual patient data meta-analysis: Evaluating convalescent plasma for COVID-19. Statistics in Medicine. 2021; 1-21. https://doi.org/10.1002/sim.9115, , , et al.
As the world faced the devastation of the COVID-19 pandemic in late 2019 and early 2020, numerous clinical trials were initiated in many locations in an effort to establish the efficacy (or lack thereof) of potential treatments. As the pandemic has been shifting locations rapidly, individual studies have been at risk of failing to meet recruitment targets because of declining numbers of eligible patients with COVID-19 encountered at participating sites. It has become clear that it might take several more COVID-19 surges at the same location to achieve full enrollment and to find answers about what treatments are effective for this disease. This paper proposes an innovative approach for pooling patient-level data from multiple ongoing randomized clinical trials (RCTs) that have not been configured as a network of sites. We present the statistical analysis plan of a prospective individual patient data (IPD) meta-analysis (MA) from ongoing RCTs of convalescent plasma (CP). We employ an adaptive Bayesian approach for continuously monitoring the accumulating pooled data via posterior probabilities for safety, efficacy, and harm. Although we focus on RCTs for CP and address specific challenges related to CP treatment for COVID-19, the proposed framework is generally applicable to pooling data from RCTs for other therapies and disease settings in order to find answers in weeks or months, rather than years.