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.
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Acknowledgments and Disclosures
Conceptualisation, E.C.; formal analysis and simulations, E.C. and C.R.; methodology, M.U. and E.C.; software: E.C. and C.R.; visualisation, E.C. and M.U.; writing, original draft, C.R. and E.C.; writing, review and editing, E.C., M.U. C.R. and V.J. This work was performed during the authors’ research time and no other funding was used. All data generated or analysed during this study are included in this published article and its supplementary information files (see Appendix A) and are available on Zenodo (https://doi.org/10.5281/zenodo.4059718).
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Comets, E., Rodrigues, C., Jullien, V. et al. Conditional Non-parametric Bootstrap for Non-linear Mixed Effect Models. Pharm Res 38, 1057–1066 (2021). https://doi.org/10.1007/s11095-021-03052-6
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DOI: https://doi.org/10.1007/s11095-021-03052-6