Adjusted adaptive Lasso for covariate model-building in nonlinear mixed-effect pharmacokinetic models

• Published in: Journal of pharmacokinetics and pharmacodynamics 2017-02, Vol.44 (1), p.55-66
• Main Authors: Haem, Elham, Harling, Kajsa, Ayatollahi, Seyyed Mohammad Taghi, Zare, Najaf, Karlsson, Mats O.
• Format: Article
• Language: English
• Subjects: Adaptive Lasso; Biochemistry; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Computer Simulation; Covariate model building; Humans; Lasso; Mixed effects modeling; Models, Biological; Models, Statistical; Multicollinearity; Multivariate Analysis; Nonlinear Dynamics; Original Paper; Pharmacokinetics; Pharmacology/Toxicology; Pharmacy; Regression Analysis; Veterinary Medicine/Veterinary Science
• ISSN: 1567-567X; 1573-8744; 1573-8744
• DOI: 10.1007/s10928-017-9504-6

One important aim in population pharmacokinetics (PK) and pharmacodynamics is identification and quantification of the relationships between the parameters and covariates. Lasso has been suggested as a technique for simultaneous estimation and covariate selection. In linear regression, it has been shown that Lasso possesses no oracle properties, which means it asymptotically performs as though the true underlying model was given in advance. Adaptive Lasso (ALasso) with appropriate initial weights is claimed to possess oracle properties; however, it can lead to poor predictive performance when there is multicollinearity between covariates. This simulation study implemented a new version of ALasso, called adjusted ALasso (AALasso), to take into account the ratio of the standard error of the maximum likelihood (ML) estimator to the ML coefficient as the initial weight in ALasso to deal with multicollinearity in non-linear mixed-effect models. The performance of AALasso was compared with that of ALasso and Lasso. PK data was simulated in four set-ups from a one-compartment bolus input model. Covariates were created by sampling from a multivariate standard normal distribution with no, low (0.2), moderate (0.5) or high (0.7) correlation. The true covariates influenced only clearance at different magnitudes. AALasso, ALasso and Lasso were compared in terms of mean absolute prediction error and error of the estimated covariate coefficient. The results show that AALasso performed better in small data sets, even in those in which a high correlation existed between covariates. This makes AALasso a promising method for covariate selection in nonlinear mixed-effect models.