And hopefully this, and the riff about the strongest case being the group as a whole and that any sub-grouping dilutes the power, results in as wide a label as possible.
IIRC, the panel as a whole were talking up up how to slice and dice the label by relative results/criteria. The speaker more or less said; Everybody benefited. If the effect appears to differentiate by some measure, but remains positive across any subgroups, how do you exclude on age, biomarker levels or risk factors? Let the doctor make the decision who to trat, given the widely positive results.
Vu - can you give a brief explanation of what p value for interaction means? That's a stat rarely discussed here, but it appears to be important for subgroup comparisons. BTW, R-IT wasn't powered to be stat sig for primary prevention - can we really draw any solid conclusions from the data, pos or neg?