A two-stage procedure is considered for obtaining fixed-width confidence intervals and optimal sample sizes for the risk ratio of two independent binomial proportions. We study desirable properties of the proposed estimator based on a bias-corrected maximum likelihood estimator (MLE). The two-stage procedure provides flexible sampling strategies, thus can be more advantageous in decision-making as well as in inference for the risk ratio. As a result, the proposed procedure can be a remedy not only for asymptotic consistency, but also for drawbacks of coverage to the nominal probability of the purely sequential method. To investigate large-sample properties of the proposed procedure, first-order asymptotic expansions are obtained. Through Monte Carlo experiments, we examine finite sample behavior for various scenarios of samples for illustrations.