Determination of an optimal dosage regimen using a Bayesian decision analysis of efficacy and adverse effect data.

Abstract

One of the aims of Phase II clinical trials is to determine the dosage regimen(s) that will be investigated during a confirmatory Phase III clinical trial. During Phase II, pharmacodynamic data are collected that enables the efficacy and safety of the drug to be assessed. It is proposed in this paper to use Bayesian decision analysis to determine the optimal dosage regimen based on efficacy and toxicity of the drug oxybutynin used in the treatment of urinary urge incontinence. Such an approach results in a general framework allowing modeling, inference and decision making to be carried out. For oxybutynin, the repeated measurement efficacy and toxicity data were modeled using nonlinear hierarchical models and inferences were based on posterior probabilities. The optimal decision in this problem was to determine the dosage regimen that maximized the posterior expected utility given the prior information on the model parameters and the patient response data. The utility function was defined using clinical opinion on the satisfactory levels of efficacy and toxicity and then combined by weighting the relative importance of each pharmacodynamic response. Markov chain Monte Carlo (MCMC) methodology implemented in Win-BUGS 1.3 was used to obtain posterior estimates of the model parameters, probabilities and utilities.