A Bayesian approach to the analysis of clinical trial data using logistic regression: example from a randomized placebo-controlled crossover trial of propranolol for migraine prevention
Christopher Dardis, Yogesh Moradiya, Arnold Eggers
SUNY Downstate Medical Center, Brooklyn, NY, USA
Abstract: Bayesian methods enable the “prior” (or informative) beliefs of an audience to be combined with the results of a clinical trial to arrive at a final "posterior" belief. This example concerns previously published data from a double-blind placebo-controlled trial of propranolol to reduce the number of episodes of migraine, where subjects were crossed-over after 3 months of treatment. The informative prior range was supplied by an educated audience (members of our Faculty of Neurology) who were given review papers on propranolol in migraine prophylaxis and placebo responses in migraine trials. We used logistic regression models to try to predict those whose symptoms improved (based on treatment or on the time period under consideration; ie, the first or second 3-month period, or based on both factors considered together). The posterior was generated using the Markov–Chain Monte–Carlo methods. For the original dataset, the Bayesian posteriors tended to be more tightly defined than those with no prior or minimally informative prior beliefs, thus yielding firmer conclusions in light of the trial. When compared with a larger dataset (which was generated from the original, but was arrived at by multiplying the number of observations by 10), the influence of prior beliefs was much less marked, but the posteriors did tend to be marginally more narrowly defined. This finding is in keeping with existing work on Bayesian methods, highlighting their value in aiding interpretation of trials with a small number of observations.
Keywords: Bayesian, migraine, randomized trial, placebo, propranolol
This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution - Non Commercial (unported, v3.0) License. By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms.Download Article [PDF]