In analyses of time-to-event outcomes, we may utilize efficient sampling designs to save effort, cost, and valuable specimens. The originally proposed case-cohort design is one such design, and performs analysis on a random sample selected from the full cohort supplemented with unsampled individuals who experienced an event. In Alzheimer’s disease biomarker discovery, such cost-savings is important as samples that contain biomarker measurements can be very limited due to high burden and cost. Furthermore, quantifying differences across subgroups and ensuring that diagnostic biomarker effects can be well powered and well understood across subpopulations is an increasingly important goal. This ability to quantify subgroup differences is required in efforts to minimize health disparities and achieve precision medicine. In this paper, we emphasize the importance of such considerations and propose using a stratified case-cohort estimator to sample a subcohort with balanced subgroups across the proposed effect modifier. We provide inverse-probability-of-sampling-based estimates that consistently estimate covariate effects in the population while improving precision for assessing differences across rare subpopulations. Simulation results are presented to illustrate power gains for testing effect modification, especially as a population subgroup becomes increasingly rare, and show minimal bias for marginal covariate effect estimates. We apply this method to data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to better assess potential heterogeneity of phosphorylated tau as a biomarker for AD progression across ethnoracial and genetic status subpopulations.
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