English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Correspondence: Christopher Dardis Department of Neurology, Barrow Neurological Institute, 350 W. Thomas Road, Phoenix, AZ 85013, USA Tel +602 406 6262 Fax +602 406 6260 Email christopherdardis@gmail.com 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.

open access medical statistics

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