Population selection is a crucial subject in clinical development nowadays as personalized medicine is growing interest. Evolution on biomarker scanning techniques allow for composition and detection of subpopulation of interest when analysing new drug responses in a disease. Seamless adaptive trials could allow for subgroup analysis with selection of the most promising population at interim analysis. We propose a hybrid Bayesian design for seamless Phase II/III trials with binary and time-to-event outcomes for the first and second phase, respectively. In this work, at interim analysis several prior distributions including shrinkage prior are compared to possibly select/discard a population, and a final test using conditional error function as a combination method testing procedure, to control the frequentist type I error, is used. Simulation studies showed that the logistic regression model performs better than frequentist testing for population selection problem when the subgroup should be selected. Shrinkage prior distributions tends to be more conservative than simpler normal distributions as studies that would have ended positive are stopped at interim analysis.