Modified Nested Case-Control Designs for Assessing Effect Modification in Underrepresented Groups

Oregon Convention Center 

The nested case-control (NCC) design is useful in biomarker discovery for rare diseases, particularly when potential biomarkers may be difficult or expensive to collect. By including all events and a subsample of controls, the NCC design maximizes information while reducing the number of subjects requiring full covariate information. Because the NCC design uses fewer controls compared to the full cohort analysis, however, it also reduces the number of patients from underrepresented groups included in the sample, further exacerbating the issue of underrepresenation in clinical research. To help alleviate this problem, we propose a weighted NCC (WNCC) sampling design that allows for oversampling of subpopulations, thereby maximizing precision for estimated covariate effects in these subpopulations. We extend the Samuelsen estimator to correct for weighted sampling and propose corrected estimation and inferential methods when oversampling is based upon both binary and continuous covariates. We apply our proposed method to data from the NIH-funded National Alzheimer's Coordinating Center (NACC) where we consider differential associations between amyloid beta and the risk of Alzheimer's disease between Black and non-Black populations.

survival analysis

efficient sampling

weighted sampling

effect modification

Alzheimer's disease

biomarkers 

Lifetime Data Science Section