Bayesian efficient safety monitoring: a simple and well-performing framework to continuous safety monitoring of adverse events in randomized clinical trials.

During randomized controlled trials, it is critical to remain vigilant in safety monitoring. A common approach is to present information over time, such as frequency tables and graphs, when analyzing adverse events. Nevertheless, there is still a need for developing statistical methods for analyzing safety data of a dynamic nature.

Continuous event monitoring via a Bayesian predictive approach.

In clinical trials, continuous monitoring of event incidence rate plays a critical role in making timely decisions affecting trial outcome. For example, continuous monitoring of adverse events protects the safety of trial participants, while continuous monitoring of efficacy events helps identify early signals of efficacy or futility. Because the endpoint of interest is often the event incidence associated with a given length of treatment duration (e.

Twenty-five years of confirmatory adaptive designs: opportunities and pitfalls.

'Multistage testing with adaptive designs' was the title of an article by Peter Bauer that appeared 1989 in the German journal Biometrie und Informatik in Medizin und Biologie. The journal does not exist anymore but the methodology found widespread interest in the scientific community over the past 25 years. The use of such multistage adaptive designs raised many controversial discussions from the beginning on, especially after the publication by Bauer and Köhne 1994 in Biometrics: Broad enthusiasm about potential applications of such designs faced critical positions regarding their statistical efficiency.