Adaptive sample size adjustment (SSA) for clinical trials consists of examining early subsets of on-trial data to adjust estimates of statistical parameters and sample size requirements. Blinded SSA is often preferred because it obviates many logistical complications of unblinded SSA and generally introduces less bias. On the other hand, current blinded SSA methods for binary data offer little to no new information about the treatment effect (TE), ignore uncertainties associated with the population treatment proportions, and/or depend on enhanced randomization schemes that risk partial unblinding. I propose a Bayesian blinded SSA method for use when the primary analysis is a non-inferiority test regarding a risk difference. It pre-trial and peri-trial evidence about the TE via Bayesian updating, while protecting the blind. I compare the method to an established one, in terms of predictive power, frequentist power, type 1 error rate, the bias of the TE estimate, and the average absolute deviation from the targeted power. I illustrate the use of the method with an example.