Non-linear mixed-effects modeling software

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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.

Contents

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

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References

  1. Davidian, Marie; Giltinan, David M. (1995-06-01). Nonlinear Models for Repeated Measurement Data. CRC Press. ISBN   978-0-412-98341-2.
  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. 46 (2): 173–192. doi:10.1007/s10928-019-09630-x. ISSN   1573-8744. PMID   30949914. S2CID   96436038.
  3. "nlme function - RDocumentation". www.rdocumentation.org. Retrieved 2022-05-09.
  4. "Nonlinear mixed-effects estimation - MATLAB nlmefit - MathWorks Benelux". nl.mathworks.com. Retrieved 2022-05-09.
  5. "Does IBM SPSS Statistics offer nonlinear mixed models?". www.ibm.com. 2020-04-16. Retrieved 2022-05-09.
  6. 1 2 "Pharmacometrics - an overview | ScienceDirect Topics". www.sciencedirect.com. Retrieved 2022-05-09.
  7. 1 2 3 4 5 6 "Pharmacokinetic Software". www.pharmpk.com. Retrieved 2022-05-09.
  8. 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.