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All Model Evaluation questions

Question 6 of 6 Advanced Scenario

How would you report evaluation uncertainty across patient subgroups?

Short answer

Report performance for each clinically relevant subgroup together with sample sizes, outcome counts and uncertainty intervals. Make clear which comparisons were planned, how groups overlap and where the data are too sparse to support strong conclusions.

Why

A single average performance estimate can hide important failures. First define the intended population and subgroups, such as age ranges, sites or disease severity, based on clinical meaning rather than choosing only interesting results after inspection. For each group report denominators, events, discrimination, calibration where feasible and confidence intervals obtained using methods consistent with the sampling structure.

Patient-level or site-level dependencies must be respected during resampling. Small groups may have unstable or undefined metrics, so disclose that limitation rather than filling the gap with precise-looking estimates. Investigate whether differences could arise from prevalence, missing data, measurement quality or genuine model behavior.

Distinguish exploratory subgroup findings from validated performance claims, and consider external validation in new settings. The point is not to prove that all subgroup metrics are equal, but to make plausible weaknesses and uncertainty visible.

An example

A model performs well overall but one clinic contributes only six positive cases. A subgroup AUROC with no uncertainty would overstate the precision of that estimate.

Common mistakes

  • Ranking demographic groups by noisy point estimates without confidence intervals or inventing certainty for sparse subgroups