Prescreen, Then Subsample: Efficient model training for large-scale biomedical data with correlated covariates
Abstract
Statistical modeling of high-dimensional and large-sample data with complex correlation structures is increasingly important in biomedical research, particularly in large-scale omics applications such as genomic studies. Analyzing these large-scale datasets imposes substantial computational and memory demands, requiring efficient model training strategies that maintain predictive accuracy. We propose Prescreen, Then Subsample (PTS), a two-stage approach that combines variable selection with optimal-design-based informative subsampling to address both high dimensionality and large sample size. The first stage screens informative variables and can explicitly account for covariate correlation; the second stage selects informative observations in the prescreened variable space. In contrast to existing two-stage subdata selection approaches, PTS can incorporate multivariable fine-mapping to identify a correlation-aware covariate space for informative subsampling. In particular, we integrate the Sum of Single Effects (SuSiE) model for fine-mapping-based prescreening with information-based optimal subdata selection (IBOSS) for sample selection. Simulation studies based on correlated Gaussian covariates and biobank-scale synthetic genotype data show that PTS reduces model training time while achieving predictive performance comparable to full-data training. We find that SuSiE-based PTS provides improved predictive performance consistent with better stage-one screening precision compared with commonly used univariate prescreening methods. PTS therefore provides a flexible and scalable training framework for large-scale biomedical data with complex correlation structures, including genomic data with linkage disequilibrium.
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BibTeX
@misc{dasgupta2026prescreen,
title = {{Prescreen, Then Subsample: Efficient model training for large-scale biomedical data with correlated covariates}},
author = {Subhadra Dasgupta and Christian Staerk},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.21916},
eprint = {2610.21916},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.21916},
}
RIS
TY - UNPB
TI - Prescreen, Then Subsample: Efficient model training for large-scale biomedical data with correlated covariates
AU - Subhadra Dasgupta
AU - Christian Staerk
PY - 2026
DA - 2026/10/15
PB - arXiv
DO - 10.48550/arxiv.2610.21916
UR - https://arxiv.org/abs/2610.21916
AB - Statistical modeling of high-dimensional and large-sample data with complex correlation structures is increasingly important in biomedical research, particularly in large-scale omics applications such as genomic studies. Analyzing these large-scale datasets imposes substantial computational and memory demands, requiring efficient model training strategies that maintain predictive accuracy. We propose Prescreen, Then Subsample (PTS), a two-stage approach that combines variable selection with optimal-design-based informative subsampling to address both high dimensionality and large sample size. The first stage screens informative variables and can explicitly account for covariate correlation; the second stage selects informative observations in the prescreened variable space. In contrast to existing two-stage subdata selection approaches, PTS can incorporate multivariable fine-mapping to identify a correlation-aware covariate space for informative subsampling. In particular, we integrate the Sum of Single Effects (SuSiE) model for fine-mapping-based prescreening with information-based optimal subdata selection (IBOSS) for sample selection. Simulation studies based on correlated Gaussian covariates and biobank-scale synthetic genotype data show that PTS reduces model training time while achieving predictive performance comparable to full-data training. We find that SuSiE-based PTS provides improved predictive performance consistent with better stage-one screening precision compared with commonly used univariate prescreening methods. PTS therefore provides a flexible and scalable training framework for large-scale biomedical data with complex correlation structures, including genomic data with linkage disequilibrium.
ER -