Design for sample size re-estimation with interim data for double-blind clinical trials with binary outcomes

Estimation of sample size in clinical trials requires knowledge of parameters that involve the treatment effect and variability, which are usually uncertain to medical researchers. The recent release within the European Union of a Note for Guidance from the Commission for Proprietary Medical Products (CPMP) highlights the importance of this issue. Most previous papers considered the case of continuous response variables that assume a normal distribution; some regarded the portion up to the interim stage as an 'internal pilot study' and required unblinding. In this paper, our concern is with the case of binary response variables, which is more difficult than the normal case since the mean and variance are not distinct parameters. We offer a design with a simple stratification strategy that enables us to verify and update the assumption of the response rates given initially in the protocol. The design provides a method to re-estimate the sample size based on interim data while preserving the trial's blinding. An illustrative numerical example and simulation results show slight effect on the type I error rate and the decision making characteristics on sample size adjustment.