Performance of the SAEM and FOCEI Algorithms in the Open-Source, Nonlinear Mixed Effect Modeling Tool nlmixr

Area of Science:

• Pharmacometrics
• Computational Statistics
• R Programming

Background:

• Nonlinear mixed-effects (NLME) models are crucial in pharmacometrics for analyzing complex biological data.
• Existing software packages like Monolix and NONMEM are industry standards for NLME model parameter estimation.
• A need exists for accessible, open-source tools that provide comparable performance.

Purpose of the Study:

• To evaluate the performance of the nlmixr R package for pharmacometric NLME model parameter estimation.
• To compare nlmixr's SAEM and FOCEI algorithms against established industry standards (Monolix and NONMEM).

Main Methods:

• The study compared nlmixr with Monolix and NONMEM using two scenarios: a simple model with sparse data and complex compartmental models with rich data.
• Performance was assessed by comparing parameter estimates and standard errors from SAEM and FOCEI algorithms in nlmixr against corresponding outputs from NONMEM/FOCEI and Monolix/SAEM.

Main Results:

• nlmixr's SAEM and FOCEI algorithms demonstrated close agreement with NONMEM/FOCEI and Monolix/SAEM in terms of parameter estimates.
• Associated standard errors from nlmixr also closely matched those from the industry-standard software.
• The performance was consistent across both simple and complex model scenarios with varying data sampling densities.

Conclusions:

• The nlmixr package provides accurate and reliable parameter estimation for pharmacometric NLME models.
• nlmixr serves as a viable, open-source alternative to commercial software for pharmacometric analysis.
• The package's uniform modeling language and comparable performance enhance its utility for researchers.