Non-linear mixed-effects modeling software: Difference between revisions
Content deleted Content added
update wikilinks |
additional wikilink |
||
| Line 1: | Line 1: | ||
'''Nonlinear mixed effects models''' are a special case of [[regression analysis]] |
'''[[Nonlinear mixed-effects model|Nonlinear mixed effects models]]''' are a special case of [[regression analysis]] for which a range of different [[software|'''software''']] solutions are available. The statistical properties of nonlinear mixed-effects models make direct estimation by a [[Gauss–Markov theorem]] impossible. Nonlinear mixed effects models are therefore estimated according to [[Maximum Likelihood]] principles.<ref>{{Cite book |last=Davidian |first=Marie |url=https://books.google.de/books?hl=en&lr=&id=0eSIBPAL4qsC&oi=fnd&pg=PR13&ots=9fwzKI5A2L&sig=0V63xikW4ReSORYaI-yt1QRqCnM&redir_esc=y#v=onepage&q&f=false |title=Nonlinear Models for Repeated Measurement Data |last2=Giltinan |first2=David M. |date=1995-06-01 |publisher=CRC Press |isbn=978-0-412-98341-2 |language=en}}</ref>. Specific estimation methods are applied, such as linearization methods as first-order (FO), first-order conditional (FOCE) or the lapplacian (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 algorithm|Metropolic-Hastings]] or the [[NUTS]] algorithms.<ref>{{Cite journal |last=Tsiros |first=Periklis |last2=Bois |first2=Frederic Y. |last3=Dokoumetzidis |first3=Aristides |last4=Tsiliki |first4=Georgia |last5=Sarimveis |first5=Haralambos |date=2019-04-01 |title=Population pharmacokinetic reanalysis of a Diazepam PBPK model: a comparison of Stan and GNU MCSim |url=https://doi.org/10.1007/s10928-019-09630-x |journal=Journal of Pharmacokinetics and Pharmacodynamics |language=en |volume=46 |issue=2 |pages=173–192 |doi=10.1007/s10928-019-09630-x |issn=1573-8744}}</ref> 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-purpose software == |
||
General (use case agnostic) nonlinear mixed effects estimation software can be covering multiple estimation methods or focus on a single. |
General (use case agnostic) nonlinear mixed effects estimation software can be covering multiple estimation methods or focus on a single. |
||
Revision as of 14:13, 9 May 2022
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 Gauss–Markov theorem 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 lapplacian (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 Metropolic-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
- SAS is a package that is used in the wide statistical community and supports multiple estimation methods from PROC NLMIX
- Multiple estimation methods are available in the R software system
- MATLAB provides multiple estimation methods in their nlmefit system[3]
SPSS at the moment does not support non-linear mixed effects methods.[4]
References
- ^ Davidian, Marie; Giltinan, David M. (1995-06-01). Nonlinear Models for Repeated Measurement Data. CRC Press. ISBN 978-0-412-98341-2.
- ^ 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.
- ^ "Nonlinear mixed-effects estimation - MATLAB nlmefit - MathWorks Benelux". nl.mathworks.com. Retrieved 2022-05-09.
- ^ "Does IBM SPSS Statistics offer nonlinear mixed models?". www.ibm.com. 2020-04-16. Retrieved 2022-05-09.