Bayesian Clinical Trial Design
Bayesian Clinical Trial designs allow clinical trials to be modified in real time based on accumulating data, using Bayesian statistics to combine prior knowledge with observed results. This placeholder content will be refined with the Cogitars team.
What is Bayesian Adaptive Design?
Bayesian Adaptive Design is a clinical trial methodology that allows pre-specified modifications — such as sample size, dose allocation, or stopping rules — to be made during the trial based on interim data, using Bayesian probability to formally combine prior information with accumulating evidence.
How does Cogitars support Bayesian adaptive trial design?
We have designed and implemented more than 75 Bayesian trials, from IND submission/protocol through submission of the Clinical Study Report.
We have deep experience engaging with regulators on these designs, including novel Bayesian methods we've developed internally.
Our goal is to use Bayesian statistics as a mathematical tool to make better use of trial data, support stronger decisions in clinical development, and ultimately help get drugs approved faster and more cheaply.
Frequently Asked Questions
A Bayesian adaptive clinical trial combines prior knowledge (from past studies or expert opinion) with accumulating trial data to guide decisions.
It allows us to continuously update the probability that a treatment works or that is safe as new patient data arrives, rather than waiting until a fixed sample size is reached to run one final analysis.
This makes trials more flexible than traditional fixed-design trials and allows us to move faster in the clinical development process.
Traditional frequentist designs rely on predetermined sample sizes, fixed hypotheses or rigid decision rules (e.g. 3+3 for dose escalation). In general, they ask "would this result be unlikely if the treatment had no effect?"
Bayesian trials, on the other hand, update continuously as data comes in, combining it with prior knowledge to estimate the probability the treatment works. This allow continuous monitoring of the data (without the need for multiplicity control) and lets the trial adapt in real time—stopping early, dropping ineffective arms, adjusting sample size.
This makes trials smaller, nimbler and, arguably, more ethical.
- Early-phase dose escalation and dose optimization studies, across all indications
- Particularly relevant in oncology, since Project Optimus delineating requirements for dose optimisation was launched
- Phase 2/3 trials with limited sample sizes or high uncertainty, tend to benefit the most from Bayesian adaptive approaches.
- Rare disease studies
- Pediatric studies
- Platform trials testing multiple treatments at once, where Bayesian rules make it easy to add, drop, or compare arms as new data comes in
We have designed and implemented more than 75 Bayesian trials, from IND submission/protocol through submission of the Clinical Study Report.
We have deep experience engaging with regulators on these designs, including novel Bayesian methods we've developed internally.
Our goal is to use Bayesian statistics as a mathematical tool to make better use of trial data, support stronger decisions in clinical development, and ultimately help get drugs approved faster and more cheaply.