Finding answers faster for COVID-19: an application of Bayesian predictive probabilities

As we evaluate therapies for COVID-19 to help improve outcomes during the pandemic, researchers need to be able to make recommendations as quickly as possible. There really is no time to lose. The Data & Safety Monitoring Board (DSMB) of COMPILE, a prospective individual patient data meta-analysis, recognizes this. They are regularly monitoring the data to determine as quickly as possible if there is a sufficiently strong signal to indicate effectiveness of convalescent plasma (CP) for hospitalized patients not on ventilation. [Read More]

Exploring the properties of a Bayesian model using high performance computing

An obvious downside to estimating Bayesian models is that it can take a considerable amount of time merely to fit a model. And if you need to estimate the same model repeatedly, that considerable amount becomes a prohibitive amount. In this post, which is part of a series (last one here) where I’ve been describing various aspects of the Bayesian analyses we plan to conduct for the COMPILE meta-analysis of convalescent plasma RCTs, I’ll present a somewhat elaborate model to illustrate how we have addressed these computing challenges to explore the properties of these models. [Read More]