Abstract: Objective We propose a subgroup identification method based on the Logistic model for data from a two-arm clinical trial with dichotomous outcome variables. In this method, binary Logistic regression models are established for each group to calculate the outcome probabilities of each patient for comparison. According to the established rules, the patients are classified into their corresponding subgroups to establish a multinomial Logistic regression model. We simulated the false rate, correct judgment rate, coincidence rate and model correct judgment rate for different sample sizes and carried out an example analysis. The results of simulation showed that for different sample sizes, the false rates of this method were below 0.07 and the correct judgment rates were all above 0.75 with adequate coincidence rates and model correct judgment rates, demonstrating the effectiveness and reliability of the proposed method for subgroup identification.