Data-Adaptive Integration With Summary Data
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866910002707431424 |
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| author | Morikawa, Kosuke Komukai, Sho Hattori, Satoshi |
| author_facet | Morikawa, Kosuke Komukai, Sho Hattori, Satoshi |
| contents | Combining an internal individual-level study with readily available external summary statistics promises major efficiency gains at minimal additional cost, yet heterogeneity between sources can bias estimates for the internal target population. We develop a generalized entropy-balancing integration strategy that calibrates external moments to the internal covariate distribution, explicitly permitting a biased external sample. Our estimator of the internal-population mean is doubly robust: it remains consistent when either the outcome-regression model or the entropy-balancing modelis correctly specified. When multiple balancing specifications are plausible, we introduce a data-adaptive selection rule. We also provide easy-to-compute, fully estimable diagnostics-based on the Mahalanobis distance and the Pearson chi-square divergence-that pinpoint when integration is guaranteed to strictly outperform the internal sample mean. The approach is implemented in the R package daisy. Simulations and an application to nationwide public-access defibrillation records in Japan demonstrate meaningful precision gains while maintaining bias control under distributional shift. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_11482 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-Adaptive Integration With Summary Data Morikawa, Kosuke Komukai, Sho Hattori, Satoshi Methodology Combining an internal individual-level study with readily available external summary statistics promises major efficiency gains at minimal additional cost, yet heterogeneity between sources can bias estimates for the internal target population. We develop a generalized entropy-balancing integration strategy that calibrates external moments to the internal covariate distribution, explicitly permitting a biased external sample. Our estimator of the internal-population mean is doubly robust: it remains consistent when either the outcome-regression model or the entropy-balancing modelis correctly specified. When multiple balancing specifications are plausible, we introduce a data-adaptive selection rule. We also provide easy-to-compute, fully estimable diagnostics-based on the Mahalanobis distance and the Pearson chi-square divergence-that pinpoint when integration is guaranteed to strictly outperform the internal sample mean. The approach is implemented in the R package daisy. Simulations and an application to nationwide public-access defibrillation records in Japan demonstrate meaningful precision gains while maintaining bias control under distributional shift. |
| title | Data-Adaptive Integration With Summary Data |
| topic | Methodology |
| url | https://arxiv.org/abs/2506.11482 |