Emmanuelle Comets1, Christelle Rodrigues2, Vincent Jullien3, Moreno Ursino4,5,6. 1. Universit́e de Paris, INSERM IAME; INSERM, CIC 1414; Rennes-1 University, France 16 rue Henri Huchard, 75018, Paris, France. emmanuelle.comets@inserm.fr. 2. INSERM U1129, Paris Descartes University, CEA, Paris, France. 3. UF Pharmacologie, GH Paris Seine Saint-Denis, Universit́e Paris, 13, Paris, France. 4. Unit of Clinical Epidemiology, Assistance Publique-Hôpitaux de Paris, CHU Robert Debré, Université de Paris, Sorbonne Paris-Cité, Inserm U1123 and CIC-EC 1426, Paris, F-75019, France. 5. Inserm, Centre de Recherche des Cordeliers, Sorbonne Université, Université de Paris, Paris, F-75006, France. 6. Inria, HeKA, F-75006, Paris, France.
Abstract
PURPOSE: Non-linear mixed effect models are widely used and increasingly integrated into decision-making processes. Propagating uncertainty is an important element of this process, and while standard errors (SE) on pa- rameters are most often computed using asymptotic approaches, alternative methods such as the bootstrap are also available. In this article, we propose a modified residual parametric bootstrap taking into account the different levels of variability involved in these models. METHODS: The proposed approach uses samples from the individual conditional distribution, and was implemented in R using the saemix algorithm. We performed a simulation study to assess its performance in different scenarios, comparing it to the asymptotic approximation and to standard bootstraps in terms of coverage, also looking at bias in the parameters and their SE. RESULTS: Simulations with an Emax model with different designs and sigmoidicity factors showed a similar coverage rate to the parametric bootstrap, while requiring less hypotheses. Bootstrap improved coverage in several scenarios compared to the asymptotic method especially for the variance param-eters. However, all bootstraps were sensitive to estimation bias in the original datasets. CONCLUSIONS: The conditional bootstrap provided better coverage rate than the traditional residual bootstrap, while preserving the structure of the data generating process.
PURPOSE: Non-linear mixed effect models are widely used and increasingly integrated into decision-making processes. Propagating uncertainty is an important element of this process, and while standard errors (SE) on pa- rameters are most often computed using asymptotic approaches, alternative methods such as the bootstrap are also available. In this article, we propose a modified residual parametric bootstrap taking into account the different levels of variability involved in these models. METHODS: The proposed approach uses samples from the individual conditional distribution, and was implemented in R using the saemix algorithm. We performed a simulation study to assess its performance in different scenarios, comparing it to the asymptotic approximation and to standard bootstraps in terms of coverage, also looking at bias in the parameters and their SE. RESULTS: Simulations with an Emax model with different designs and sigmoidicity factors showed a similar coverage rate to the parametric bootstrap, while requiring less hypotheses. Bootstrap improved coverage in several scenarios compared to the asymptotic method especially for the variance param-eters. However, all bootstraps were sensitive to estimation bias in the original datasets. CONCLUSIONS: The conditional bootstrap provided better coverage rate than the traditional residual bootstrap, while preserving the structure of the data generating process.
Authors: R L Lalonde; K G Kowalski; M M Hutmacher; W Ewy; D J Nichols; P A Milligan; B W Corrigan; P A Lockwood; S A Marshall; L J Benincosa; T G Tensfeldt; K Parivar; M Amantea; P Glue; H Koide; R Miller Journal: Clin Pharmacol Ther Date: 2007-05-23 Impact factor: 6.875