Developing a Bayesian hierarchical model for a prospective individual patient data meta-analysis with continuous monitoring.

ABSTRACT:

Background

Numerous clinical trials have been initiated to find effective treatments for COVID-19. These trials have often been initiated in regions where the pandemic has already peaked. Consequently, achieving full enrollment in a single trial might require additional COVID-19 surges in the same location over several years. This has inspired us to pool individual patient data (IPD) from ongoing, paused, prematurely-terminated, or completed randomized controlled trials (RCTs) in real-time, to find an effective treatment as quickly as possible in light of the pandemic crisis. However, pooling across trials introduces enormous uncertainties in study design (e.g., the number of RCTs and sample sizes might be unknown in advance). We sought to develop a versatile treatment efficacy ass

SUBMITTER: Wu D 

PROVIDER: S-EPMC9875783 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature