title: From P-Values to Objective Probabilities in Assessing Medical Treatments.
abstract: The assessment of the effectiveness of a treatment in a clinical trial, depends on calculating p-values. However, p-values are only indirect and partial indicators of a genuine effect. Particularly in situations where publication bias is very likely, assessment using a p-value of 0.05 may not be sufficiently cautious. In other situations it seems reasonable to believe that assessment based on p-values may be unduly conservative. Assessments could be improved by using prior information. This implies using a Bayesian approach to take account of prior probability. However, the use of prior information in the form of expert opinion can allow bias. A method is given here that applies to assessments already included or likely to be included in the Cochrane Collaboration, excluding those reviews concerning new drugs. This method uses prior information and a Bayesian approach, but the prior information comes not from expert opinion but simply from the distribution of effectiveness apparent in a random sample of summary statistics in the Cochrane Collaboration. The method takes certain types of summary statistics and their confidence intervals and with the help of a graph, translates this into probabilities that the treatments being trialled are effective.
authors.affiliation: Discipline of Mathematics, College of Science, Technology and Engineering, James Cook University, Townsville, Australia.
authors.forename: David
authors.initials: D
authors.lastname: Kault
authors.affiliation: Centre for Applied Health Economics, Menzies Health Institute, Griffith University, Brisbane, Australia.
authors.forename: Sam
authors.initials: S
authors.lastname: Kault
mesh_terms: D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D006801:Humans; D011336:Probability; D016896:Treatment Outcome