Abstract Number:
3318
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Mikaela Nishida (1), Michelle Nuño (2), Daniel Gillen (3)
Institutions:
(1) University of California, Irvine, Irvine, CA, (2) Children's Oncology Group, Monrovia, CA, Los Angeles, CA, (3) University of California-Irvine, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
The nested case-control (NCC) design is useful in biomarker discovery for rare events, particularly when potential biomarkers may be difficult to collect. By including all events and a subsample of controls, the NCC design maximizes information while reducing the number of subjects requiring the collection of full covariate information. Since 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 underrepresentation in clinical research. To alleviate this issue, we propose a weighted NCC 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 correct estimation and inferential methods when oversampling is based on binary and continuous covariates. We apply our method to National Alzheimer's Coordinating Center data where we consider differential associations between amyloid beta and the risk of Alzheimer's disease between Black and non-Black populations.
Keywords:
survival analysis|efficient sampling|weighted sampling|effect modification|Alzheimer's disease|biomarkers
Sponsors:
Lifetime Data Science Section
Tracks:
Miscellaneous
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