We propose a Bayesian approach to monitor clinical trials with clustered binary outcomes using multivariate probit models. Our monitoring is based on the calculated probability of the reduced incidence rate using a new treatment compared with the standard treatment greater than a target improvement under different prior scenarios for the treatment effect. We develop a Bayesian sampling algorithm for posterior inference allowing missing values in the outcomes. We illustrate our method using a published early trail of inhaled nitric oxide therapy in premature infants. © 2008 Elsevier Inc. All rights reserved.