When randomized trial results are available for several different groups of patients, neither applying the overall results to each type of patient nor using group-specific results is entirely satisfactory. Instead, we estimate group-specific treatment effects using a Bayesian approach with informative priors for the treatment x group interactions. We describe how we elicited these prior beliefs about the effects of a new drug for the treatment of heart failure in three different patient groups. Using results from three trials, one in each patient group, the posterior mean treatment effects are very similar to the trial-specific maximum likelihood estimates, showing that in this case each trial effectively stands by itself. Our methods can also be applied to subgroup analyses in a single clinical trial, where subgroup-specific posterior means are likely to lie between the subgroup-specific maximum likelihood estimates and the pooled maximum likelihood estimates.