Bayesian Approaches in Early Clinical Research

What knowledge gaps exist regarding the implementation of these new analytical techniques, and how can statisticians and data scientists bridge them to maximize effectiveness of clinical research?

Despite the promises of Bayesian approaches, the lack of expert training, software tools, and computational resources has made their adoption slow. Statisticians and data scientists should invest in training programs to enhance their skills in Bayesian modeling, use open-source software for Bayesian modeling, and ensure that they have access to well trained computational analysts. 

It is also essential that statisticians and data scientists effectively communicate the results of these analyses to clinicians, regulatory authorities, and maybe even patient groups to encourage adoption.

Bayesian approaches have great potential to improve the accuracy and efficiency of data analysis in early-phase clinical trials. But Bayesian methods are not without their challenges, but with adequate training, resources, and communication, they provide opportunities for novel ways to cope with complex problems in clinical research.

But, first comes the challenge: prior elicitation from clinical information. Developing prior distributions can be difficult without the right set of tools and resources.

In this episode, Miguel Pereira, a statistical consultant for a German-based company COGITARS specializing in early clinical trial design, and I highlighted ways to tackle these challenges.

We also discuss the following points: