Individual Influence in Population Pharmacokinetics Depends on Underlying Mechanism: Evidence from a Jackknife ΔOFV Approach

Background: Identifying influential individuals is a critical step in nonlinear mixed-effects modeling, yet commonly used diagnostics primarily reflect local model fit or parameter perturbation and may fail to capture the full impact of individual data on model estimation. Methods: We conducted a simulation study based on a one-compartment pharmacokinetic model with first-order absorption. Four scenarios were investigated: a reference scenario without induced influence, a residual outlier scenario, a structurally influential individual with enriched sampling, and a latent subpopulation scenario. For each scenario, 25 datasets of 100 individuals were simulated, and individual influence was assessed using a leave-one-out jackknife approach. Influence metrics included the change in objective function value (ΔOFV) and parameter perturbation measures. Detection performance was evaluated at both subject and dataset levels. Results: All jackknife runs were successfully completed and analyzable. Residual outliers were consistently identified by both ΔOFV and parameter-based metrics. In contrast, structurally influential individuals were reliably detected by ΔOFV (median rank = 1) but not by parameter-based metrics (median rank ≈ 25). Across scenarios, the association between ΔOFV and parameter perturbation was weak, and nearly absent in the structurally influential scenario. In the latent subpopulation scenario, individual-level detection was limited, but dataset-level detection remained effective, with at least one subpopulation member frequently identified among top-ranked individuals. Conclusions: Individual influence in nonlinear mixed-effects models is strongly mechanism-dependent. Jackknife-based ΔOFV provides a direct and general measure of individual influence, capable of detecting both residual outliers and structurally influential individuals. In contrast, parameter-based metrics quantify parameter sensitivity rather than individual influence and may therefore overlook influential subjects in specific contexts. These findings support the use of jackknife deletion as a reference approach for influence assessment in pharmacometric workflows.