The recent controversy over the increased risk of venous thrombosis with third generation oral contraceptives illustrates the public policy dilemma that can be created by relying on conventional statistical tests and estimates : case-control studies showed a significant increase in risk and forced a decision either to warn or not to warn.
Conventional statistical tests are an improper basis for such decisions because they dichotomise results according to whether they are or are not significant and do not allow decision makers to take explicit account of additional evidence-for example, of biological plausibility or of biases in the studies.
A Bayesian approach overcomes both these problems.
A Bayesian analysis starts with a « prior » probability distribution for the value of interest (for example, a true relative risk) - based on previous knowledge-and adds the new evidence (via a model) to produce a « posterior » probability distribution.
Because different experts will have different prior beliefs sensitivity analyses are important to assess the effects on the posterior distributions of these differences.
Sensitivity analyses should also examine the effects of different assumptions about biases and about the model which links the data with the value of interest.
One advantage of this method is that it allows such assumptions to be handled openly and explicitly. (...)
Mots-clés Pascal : Statistique test, Test Bayes, Loi probabilité, Analyse sensibilité, Homme
Mots-clés Pascal anglais : Test statistic, Bayes test, Probability distribution, Sensitivity analysis, Human
Notice produite par :
Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 96-0429012
Code Inist : 002B28F. Création : 10/04/1997.