ArticlePharmaceutical research2026
Federated Estimation of Population Pharmacokinetic Models with Exact Equivalence to Pooled-Data Analysis.
Article in Pharmaceutical research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
6 authors.
Funding
Abstract
purposePopulation pharmacokinetic (PopPK) analysis relies on nonlinear mixed-effects modeling of pooled data; however, sharing subject-level data across sites is often restricted. This study develops a federated framework that exactly recovers the global objective function corresponding to centralized pooled-data analysis, thereby yielding identical parameter estimates without sharing subject-level data.
methodsEach site computes its local contributions to the global objective function and corresponding gradients. A central server aggregates these quantities to reconstruct the global objective function and gradient at each parameter value. Performance was evaluated using simulations of a one-compartment oral model under two multi-site scenarios: heterogeneous sparse sampling and between-site heterogeneity in covariate distribution. Federated estimation was compared with centralized pooled-data estimation, independent site-specific estimation, and weighted combination of site-specific estimates and variances. Parameter uncertainty was assessed using bootstrap resampling.
resultsAcross scenarios, federated estimation yielded parameter estimates and bootstrap confidence intervals that closely matched with those from centralized pooled-data estimation for fixed effects, between-subject variability, and residual error. In contrast, independent site-specific estimation showed deviations under certain conditions, and weighted pooling of site-specific estimates did not reproduce centralized variance estimates.
conclusionsThe proposed federated framework enables mathematically equivalent PopPK estimation without sharing subject-level data.
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