Often in observational studies the treatment effects within subgroups are important. The question arises whether the overall propensity score (PS) should be used to adjust for confounding and to estimate subgroup treatment effects or whether PS should be recalculated within the subgroups to estimate subgroup treatment effects. This paper addresses this issue from the perspective of the PS differences and differences for the within subgroup adjusted treatment effects. A specific real world evidence oncology study is used to illustrate the findings. We show that the propensity scores obtained from the within group model are identical to the propensity scores obtained from the overall model with the addition of the interaction effects of the subgroup variable with the other confounders. This information being added to the overall propensity score model is small. Thus, to analyze the treatment effect within subgroups, either the overall propensity score or the within subgroup propensity score will yield adjusted treatment effects which are not substantively different. In addition, for both the within subgroup PS and the overall PS, the treatment effects from the within subgroups analysis are identical to the treatment effects form the overall model and including propensity score by age interaction. We conclude that it is not necessary to compute within subgroup propensity scores nor to use within subgroup analyses to estimate the adjusted within subgroup treatment effects. This is consistent with the primary analysis which considers overall confounders and not how confounders may differ within subgroups.