biostudies-literatureUnknownKnight PNIMH NIH HHSNIGMS NIH HHSStatistical 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.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North Americahttps://www.ebi.ac.uk/biostudies/studies/S-EPMC12685035biostudies-literatureFast and robust invariant generalized linear models.PMC12685035R01 GM148494R01 MH137218Knight PDuan RJobe NIfalseFast and robust invariant generalized linear models.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.2025-01-01T00:00:00Z2025 Nov2026-06-11T03:18:08.257Z2026-06-11T03:08:16.01ZS-EPMC126850354136788410.1080/10618600.2025.2592762