With limited computing resources compared with the exponentially growing data volume, subsampling-based methods have demonstrated pervasive potential in making better use of a fixed amount of computing power. In this talk, focusing on logistic regression, I will discus the problem of statistical inference for the Optimal Subsampling Method under the A-optimality Criterion (OSMAC). For OSMAC, the subsampling probabilities depend on unknown parameters, so a two-step adaptive algorithm is used. Consistency and asymptotic normality of the estimators from the two-step adaptive algorithm are established, and the asymptotic variance-covariance matrices for different estimators are compared. Extensive numerical results are also provided to demonstrate practical performance of the estimators based on OSMAC.