New Stats4PT Lesson: Bayesian Applications in Research and Evidence Synthesis
Stats4PT Lesson Four
The latest lesson in the Stats4PT series is now live:
Bayesian Applications in Research and Evidence Synthesis
In this post, we explore how Bayesian methods can transform the way we conduct and interpret clinical research—across clinical trials, meta-analyses, causal modeling, and realist reviews.
((This is where I should have uploaded an image to make the post flashier. To be honest- I’m not really going for flash. So I opted out of adding an image. People that want to read the post will read it, drawing someone in with an image just for the sake of having an image probably won’t result in a worthwhile reading. So I’ve decided - along with much on social media - to stop. The Chaos Machine.))
Topics include:
How Bayesian clinical trials offer adaptive, real-time decision-making
Why Bayesian meta-analysis produces richer, more clinically relevant insights
The advantages of Bayesian causal modeling and its alignment with real-world reasoning
How Bayesian logic supports critical realist reviews—moving beyond “what works” to explore why and for whom interventions are effective
We also introduce future directions in Models4PT, a project designed to make Bayesian inference and causal modeling more accessible for clinicians and researchers.
If you’re just joining the series, start here:
The next lesson will focus on how Bayes’ Theorem applies directly to clinical decision-making, helping you refine diagnostic, treatment, and prognostic reasoning in the clinic.
As always, feedback and questions are welcome!