%0 Journal Article %1 Ribbing.2007 %A Ribbing, Jakob %A Nyberg, Joakim %A Caster, Ola %A Jonsson, E. Niclas %D 2007 %J Journal of Pharmacokinetics and Pharmacodynamics %K Agentspharmacokinetics Algorithms Analysis Chlormethiazolepharmacokinetics Computer Dynamics Humans Linear Models Models, Neuroprotective Nonlinear Pharmacokinetics Regression Simulation Statistical Strokedrug therapy %N 4 %P 485-517 %T The lasso--a novel method for predictive covariate model building in nonlinear mixed effects models %U doi:10.1007/s10928-007-9057-1 %V 34 %X Covariate models for population pharmacokinetics and pharmacodynamics are often built with a stepwise covariate modelling procedure (SCM). When analysing a small dataset this method may produce a covariate model that suffers from selection bias and poor predictive performance. The lasso is a method suggested to remedy these problems. It may also be faster than SCM and provide a validation of the covariate model. The aim of this study was to implement the lasso for covariate selection within NONMEM and to compare this method to SCM.In the lasso all covariates must be standardised to have zero mean and standard deviation one. Subsequently, the model containing all potential covariate-parameter relations is fitted with a restriction: the sum of the absolute covariate coefficients must be smaller than a value, t. The restriction will force some coefficients towards zero while the others are estimated with shrinkage. This means in practice that when fitting the model the covariate relations

@article{Ribbing.2007, abstract = {Covariate models for population pharmacokinetics and pharmacodynamics are often built with a stepwise covariate modelling procedure (SCM). When analysing a small dataset this method may produce a covariate model that suffers from selection bias and poor predictive performance. The lasso is a method suggested to remedy these problems. It may also be faster than SCM and provide a validation of the covariate model. The aim of this study was to implement the lasso for covariate selection within NONMEM and to compare this method to SCM.In the lasso all covariates must be standardised to have zero mean and standard deviation one. Subsequently, the model containing all potential covariate-parameter relations is fitted with a restriction: the sum of the absolute covariate coefficients must be smaller than a value, t. The restriction will force some coefficients towards zero while the others are estimated with shrinkage. This means in practice that when fitting the model the covariate relations}, added-at = {2010-02-04T16:31:48.000+0100}, author = {Ribbing, Jakob and Nyberg, Joakim and Caster, Ola and Jonsson, E. Niclas}, biburl = {https://www.bibsonomy.org/bibtex/288eb54c666b9f63001a7b5c5858d6486/hruehs}, interhash = {603ddeba1edebed32086f46e223ad7ed}, intrahash = {88eb54c666b9f63001a7b5c5858d6486}, journal = {Journal of Pharmacokinetics and Pharmacodynamics}, keywords = {Agentspharmacokinetics Algorithms Analysis Chlormethiazolepharmacokinetics Computer Dynamics Humans Linear Models Models, Neuroprotective Nonlinear Pharmacokinetics Regression Simulation Statistical Strokedrug therapy}, number = 4, pages = {485-517}, timestamp = {2010-02-04T16:31:51.000+0100}, title = {The lasso--a novel method for predictive covariate model building in nonlinear mixed effects models}, url = {doi:10.1007/s10928-007-9057-1}, volume = 34, year = 2007 }