title: [Bayesian thinking on its way into medical statistics?].
abstract: BACKGROUND
  Bayesian statistical analysis is a paradigm quite different from traditional statistical inference. We wanted to show the usefulness of this approach for some medical problems.
  
  
  METHODS
  We started with Bayes equation as it is used for estimating the probability of illness based on a specific laboratory test. We also looked into a recent Cochrane report on mammography that accepted two studies as valid and five others as biased. In comparison we used examples of clinical trials from other areas that have been misinterpreted by the use of a traditional statistical approach only.
  
  
  RESULTS
  We found that by taking into account our prior beliefs about the likely effects on breast cancer mortality of routine radiological screening programmes, the new data fit well into an estimate of a 5% mortality reduction with a 77% chance that there is a positive effect of screening.
  
  
  CONCLUSIONS
  Bayesian statistics is helpful in making decisions on the basis of experimental evidence by taking into account our prior knowledge, whereas p-values in traditional statistics only give information on how often we will end up with a false positive conclusion in the long run.
authors.affiliation: Institutt for farmakoterapi Postboks 1065 Blindern. i.a.aursnes@ioks.uio.no
authors.forename: Ivar
authors.initials: I
authors.lastname: Aursnes
authors.forename: Bent
authors.initials: B
authors.lastname: Natvig
authors.forename: Ingunn Fride
authors.initials: IF
authors.lastname: Tvete
mesh_terms: D001499:Bayes Theorem; D015519:Bone Density / Q000187:drug effects; D001943:Breast Neoplasms / Q000000981:diagnostic imaging / Q000401:mortality; D003627:Data Interpretation, Statistical; D005260:Female; D005598:Fractures, Spontaneous / Q000209:etiology / Q000517:prevention & control; D006801:Humans; D008327:Mammography; D010024:Osteoporosis / Q000150:complications / Q000188:drug therapy; D011336:Probability