Hospitals often need to know whether a new prognostic model outperforms an established one before survival outcomes in their own patients are available. Without these outcomes, the performance difference is only partially identified: it lies in an interval whose width reflects how far the target population may depart from the source. We show that this interval should be built for the difference itself, not by bounding each model's risk separately, because both risks depend on the same unknown outcomes. For the fixed-horizon Brier-risk difference, under bounded shifts in conditional log-odds that may vary in sign and magnitude across patient profiles, we derive the sharp identified set, prove that it is never wider than separate-risk subtraction, characterize exactly when it is narrower, and give a diagnostic, computable without target outcomes, for when a single-shift model narrows the interval only by assuming homogeneity. Censoring-adjusted simultaneous bootstrap envelopes turn these sets into decisions. Across 32 nonzero-sensitivity comparisons in lung cancer, separate-risk bounds were 4.0-11.1 times as wide as direct identified sets, although sampling uncertainty with 35 source deaths prevented directional certification. In a Rotterdam-to-German Breast Cancer Study Group analysis, direct identification supported the candidate model in a comparison that separate subtraction left inconclusive. Hospitals can thus obtain evidence, before local outcomes exist, on whether a candidate model predicts more accurately under explicitly stated population differences.

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