Evaluation of FOCEI and SAEM Estimation Methods in Population Pharmacokinetic Analysis Using NONMEM Across Rich, Medium, and Sparse Sampling Data

Background and Objectives: First-order conditional estimation with interaction (FOCEI) is one of the most commonly used estimation methods in nonlinear mixed effects modeling, while the stochastic approximation expectation maximization (SAEM) is the newer estimation algorithm. This work aimed to compare the performance of FOCEI and SAEM methods when using NONMEM with the classical one- and two-compartment models across rich, medium, and sparse data. Methods: One- and two-compartment models of the previous studies were used to simulate data in three scenarios: rich, medium, and sparse data. For each scenario, there were 100 data sets, containing 100 individuals in each data set. Every data set was estimated with both FOCEI and SAEM methods. The simulation and estimation were performed using NONMEM . The completion rates, percentage of relative estimation errors (%RERs), root mean square errors (RMSEs), and runtimes were considered to assess the completion, accuracy, precision, and speed of estimation, respectively. Results: Both FOCEI and SAEM methods provided comparable completion rates, median %RERs (ranged from − 9.03 to 3.27% for FOCEI and − 9.17 to 3.27% for SAEM) and RMSEs (ranged from 0.0004 to 1.244 for FOCEI and 0.0004 to 1.131 for SAEM) for most parameters in both models across three scenarios. The run times were much shorter with FOCEI (ranged from 0.18 to 0.98 min) compared to SAEM method (ranged from 4.64 to 12.03 min). Conclusions: For the classical one- and two-compartment models, FOCEI method exhibited comparable performance similar to SAEM method but with significantly shorter runtimes across rich, medium, and sparse sampling scenarios.