  Combination of several anti-cancer treatments has typically been presumed to
have enhanced drug activity. Motivated by a real clinical trial, this paper
considers phase I-II dose finding designs for dual-agent combinations, where
one main objective is to characterize both the toxicity and efficacy profiles.
We propose a two-stage Bayesian adaptive design that accommodates a change of
patient population in-between. 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-effects 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-effects distribution is specified for the main effects parameters to
capture uncertainty about the between-stage differences. The inclusion of
nonexchangeability assumption further enables that the stage-specific efficacy
parameters have their own priors. The proposed methodology is assessed with an
extensive simulation study. Our results suggest a general improvement of the
operating characteristics for the efficacy assessment, under a conservative
assumption about the exchangeability of the parameters \textit{a priori}
