In this webinar, after a very brief review of Bayesian adaptive clinical trial methods, we introduce our Bayesian responder approach to one-arm clinical trials in rare disease modeling, investigating the impact of both static and transient placebo effects.
| On-Demand | 1 hour | Your Desk! |
Arnaud MonseurSenior Manager Statistics, PharmaLex |
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Brad CarlinSenior Advisor, Data Science, PharmaLex |
We then go on to describe two-arm versions that incorporate a small concurrent placebo group, but still borrow strength from the natural history data. We also propose more traditional Bayesian changepoint models that specify a parametric functional form for the patient’s post-intervention trajectory, which in turn allow quantification of the treatment benefit in terms of the model parameters, rather than semiparametrically in terms of a response relative to some “null” model. Our results indicate that our two-arm responder and changepoint methods can offer protection against placebo effects, improving power while controlling the trial’s Type I error rate. We offer illustrations in the context of a clinical trial in a particular rare disease, where large patient-to-patient and visit-to-visit heterogeneity can be observed. In such settings, our innovative Bayesian techniques facilitate increased power to detect an effect with respect to more classical methods. We also offer advice regarding computational approaches in these settings, as well as our experience with key regulatory authorities, dialog with whom of course remains crucial in rare disease research.