Notes on Bayesian adaptive design, aka the future
Notes from a biostats seminar today by Kristine Broglio, M.S., statistical scientists at Berry Consultants. I was expecting another biostatistics talk where I would follow 10% and glaze over aggressive looking letter salads of the Greek alphabet the rest of the time. Instead, I heard about THE FUTURE. This is how controlled randomized trials will look like in the future, I am sure. It’s too spectacular not to.
fixed design is based on the math we were good at doing 200 years ago
– adaptive randomization (randomize to different groups)
– statistical modeling
can chase random highs by randomizing more patients to it, which will smooth random deviations, so can treat more patients in the best arm
Roger Perlmutter (Merck executive): we do 21st century biology in our laboratories and then do clinical trials that Hippocrates would have been comfortable with
would you rather be the last person enrolled in a trial or the first person to receive treatment?
Bayesian adaptive design shortens the gap between the two options.
would you rather be the first person enrolled in a trial or the last person in enrolled in the same trial?
clearly the latter.
are able to determine which arm of treatment is most likely to come out as the winner during interim analyses.
can stop more trials earlier because trial is more efficient
bayesian is a natural way to think about adaption. bayesian comparative design is how drs think anyway, but not limited to it. but frequentist can also use this design.
clinicians loved it, patients loved it, commercial statisticians loved it. only academic statisticians were not sure about it.
uses almost no priors, or really non-informative ones