Non-linear mixed-effects modeling software explained

Nonlinear mixed-effects models are a special case of regression analysis for which a range of different software solutions are available. The statistical properties of nonlinear mixed-effects models make direct estimation by a BLUE estimator impossible. Nonlinear mixed effects models are therefore estimated according to Maximum Likelihood principles.[1] Specific estimation methods are applied, such as linearization methods as first-order (FO), first-order conditional (FOCE) or the laplacian (LAPL), approximation methods such as iterative-two stage (ITS), importance sampling (IMP), stochastic approximation estimation (SAEM) or direct sampling. A special case is use of non-parametric approaches. Furthermore, estimation in limited or full Bayesian frameworks is performed using the Metropolis-Hastings or the NUTS algorithms.[2] Some software solutions focus on a single estimation method, others cover a range of estimation methods and/or with interfaces for specific use cases.

General-purpose software

General (use case agnostic) nonlinear mixed effects estimation software can be covering multiple estimation methods or focus on a single.

Software with multiple estimation methods

SPSS at the moment does not support non-linear mixed effects methods.[5]

Software dedicated to a single estimation method

Software dedicated to pharmacometrics

The field of pharmacometrics relies heavily on nonlinear mixed effects approaches and therefore uses specialized software approaches.[6] As with general-purpose software, implementations of both single or multiple estimation methods are available. This type of software relies heavily on ODE solvers.

Software with multiple estimation methods

Software dedicated to a single estimation method

Related software

Notes and References

  1. Book: Davidian . Marie . Nonlinear Models for Repeated Measurement Data . Giltinan . David M. . 1995-06-01 . CRC Press . 978-0-412-98341-2 . en.
  2. Tsiros . Periklis . Bois . Frederic Y. . Dokoumetzidis . Aristides . Tsiliki . Georgia . Sarimveis . Haralambos . 2019-04-01 . Population pharmacokinetic reanalysis of a Diazepam PBPK model: a comparison of Stan and GNU MCSim . Journal of Pharmacokinetics and Pharmacodynamics . en . 46 . 2 . 173–192 . 10.1007/s10928-019-09630-x . 30949914 . 96436038 . 1573-8744.
  3. Web site: nlme function - RDocumentation . 2022-05-09 . www.rdocumentation.org.
  4. Web site: Nonlinear mixed-effects estimation - MATLAB nlmefit - MathWorks Benelux . 2022-05-09 . nl.mathworks.com.
  5. Web site: 2020-04-16 . Does IBM SPSS Statistics offer nonlinear mixed models? . 2022-05-09 . www.ibm.com . en.
  6. Web site: Pharmacometrics - an overview ScienceDirect Topics . 2022-05-09 . www.sciencedirect.com.
  7. Web site: Pharmacokinetic Software . 2022-05-09 . www.pharmpk.com.
  8. Book: Wang, Matthew Fidler, Teun M. Post, Richard Hooijmaijers, Rik Schoemaker, Mirjam N. Trame, Justin Wilkins, Yuan Xiong and Wenping . nlmixr: an R package for population PKPD modeling.