NDLI: Performance Comparison of Various Maximum Likelihood Nonlinear Mixed-Effects Estimation Methods for Dose

Content Provider	Springer Nature Link
Author	Plan, Elodie L. Maloney, Alan Mentré, France Karlsson, Mats O. Bertrand, Julie
Copyright Year	2012
Abstract	Estimation methods for nonlinear mixed-effects modelling have considerably improved over the last decades. Nowadays, several algorithms implemented in different software are used. The present study aimed at comparing their performance for dose–response models. Eight scenarios were considered using a sigmoid E $_{max}$ model, with varying sigmoidicity and residual error models. One hundred simulated datasets for each scenario were generated. One hundred individuals with observations at four doses constituted the rich design and at two doses, the sparse design. Nine parametric approaches for maximum likelihood estimation were studied: first-order conditional estimation (FOCE) in NONMEM and R, LAPLACE in NONMEM and SAS, adaptive Gaussian quadrature (AGQ) in SAS, and stochastic approximation expectation maximization (SAEM) in NONMEM and MONOLIX (both SAEM approaches with default and modified settings). All approaches started first from initial estimates set to the true values and second, using altered values. Results were examined through relative root mean squared error (RRMSE) of the estimates. With true initial conditions, full completion rate was obtained with all approaches except FOCE in R. Runtimes were shortest with FOCE and LAPLACE and longest with AGQ. Under the rich design, all approaches performed well except FOCE in R. When starting from altered initial conditions, AGQ, and then FOCE in NONMEM, LAPLACE in SAS, and SAEM in NONMEM and MONOLIX with tuned settings, consistently displayed lower RRMSE than the other approaches. For standard dose–response models analyzed through mixed-effects models, differences were identified in the performance of estimation methods available in current software, giving material to modellers to identify suitable approaches based on an accuracy-versus-runtime trade-off.
Starting Page	420
Ending Page	432
Page Count	13
File Format	PDF
ISSN	12341234
Journal	The AAPS Journal
Volume Number	14
Issue Number	3
e-ISSN	15507416
Language	English
Publisher	Springer US
Publisher Date	2012-04-14
Publisher Institution	American Association of Pharmaceutical Scientists
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Pharmacy Biochemistry Pharmacology/Toxicology Biotechnology
Content Type	Text
Resource Type	Article

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