GRAPHICAL ELICITATION OF A PRIOR DISTRIBUTION FOR A CLINICAL-TRIAL

Bayesian methods are potentially useful for the design, monitoring and analysis of clinical trials. These methods, however, require that prior information be quantified and that the methods be robust. This paper describes a method to help quantify beliefs in the form of a prior distribution about regression coefficients in a proportional hazards regression model. The method uses dynamic graphical displays of probability distributions that can be freehand adjusted. The method was developed for, and is applied to, a randomized trial comparing prophylaxes for toxoplasmosis in a population of HIV-positive individuals. Prior distributions from five AIDS experts are elicited. The experts represent a community of consumers of the results of the trial and these prior distributions can be used to try to make the monitoring and analysis of the trial robust.