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Motivated by a real clinical trial, this paper considers phase I\u2013II dose finding designs for dual\u2010agent combinations, where one main objective is to characterize both the toxicity and efficacy profiles. We propose a two\u2010stage Bayesian adaptive design that accommodates a change of patient population in\u2010between. In stage I, we estimate a maximum tolerated dose combination using the escalation with overdose control (EWOC) principle. This is followed by a stage II, conducted in a new yet relevant patient population, to find the most efficacious dose combination. We implement a robust Bayesian hierarchical random\u2010effects model to allow sharing of information on the efficacy across stages, assuming that the related parameters are either exchangeable or nonexchangeable. Under the assumption of exchangeability, a random\u2010effects distribution is specified for the main effects parameters to capture uncertainty about the between\u2010stage differences. The inclusion of nonexchangeability assumption further enables that the stage\u2010specific efficacy parameters have their own priors. The proposed methodology is assessed with an extensive simulation study. 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