![]() ![]() The simulation and estimation were performed using NONMEM ®. Every data set was estimated with both FOCEI and SAEM methods. For each scenario, there were 100 data sets, containing 100 individuals in each data set. ![]() One- and two-compartment models of the previous studies were used to simulate data in three scenarios: rich, medium, and sparse data. 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. 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. ![]()
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