gms | German Medical Science

In the context of rare disease and therefore limited resources in terms of patients for clinical trials, it

is often assumed that Bayesian analysis is a possibility to achieve evidence for efficacy of new drugs

or therapy principles more efficiently. The main reason for this assumption is the opportunity to use

additional information through a prior believe or knowledge about this efficacy. However, little is said

about the true impact of this prior assumption about efficacy on the evaluation of outcome.

Using example studies we give an overview of different problems connected to Bayesian analysis with

informative and non-informative prior probabilities. We know that using non-informative priors

usually does not lead to substantially different conclusions than in the frequentist approach, although

even the assumption of non-informative priors can promote the aim of a study to demonstrate either

non-inferiority or superiority. When using informative priors based on data from other studies these

may completely determine conclusions of the trial at hand. It is thus important to balance the impact

of the prior assumption against the information to be derived in the current trial, so that collected

data still has a chance to change prior believe if substantially different.