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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.02611 |
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| _version_ | 1866929740613419008 |
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| author | Knight, Parker Jobe, Ndey Isatou Duan, Rui |
| author_facet | Knight, Parker Jobe, Ndey Isatou Duan, Rui |
| contents | Statistical integration of diverse data sources is an essential step in the building of generalizable prediction tools, especially in precision health. The invariant features model is a new paradigm for multi-source data integration which posits that a small number of covariates affect the outcome identically across all possible environments. Existing methods for estimating invariant effects suffer from immense computational costs or only offer good statistical performance under strict assumptions. In this work, we provide a general framework for estimation under the invariant features model that is computationally efficient and statistically flexible. We also provide a robust extension of our proposed method to protect against possibly corrupted or misspecified data sources. We demonstrate the robust properties of our method via simulations, and use it to build a transferable prediction model for end stage renal disease using electronic health records from the All of Us research program. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02611 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fast and robust invariant generalized linear models Knight, Parker Jobe, Ndey Isatou Duan, Rui Methodology Applications Computation Statistical integration of diverse data sources is an essential step in the building of generalizable prediction tools, especially in precision health. The invariant features model is a new paradigm for multi-source data integration which posits that a small number of covariates affect the outcome identically across all possible environments. Existing methods for estimating invariant effects suffer from immense computational costs or only offer good statistical performance under strict assumptions. In this work, we provide a general framework for estimation under the invariant features model that is computationally efficient and statistically flexible. We also provide a robust extension of our proposed method to protect against possibly corrupted or misspecified data sources. We demonstrate the robust properties of our method via simulations, and use it to build a transferable prediction model for end stage renal disease using electronic health records from the All of Us research program. |
| title | Fast and robust invariant generalized linear models |
| topic | Methodology Applications Computation |
| url | https://arxiv.org/abs/2503.02611 |