Abstract
Parameter variation in pharmacometric analysis studies can be characterized as within subject parameter variability (WSV) in pharmacometric models. WSV has previously been successfully modeled using inter-occasion variability (IOV), but also stochastic differential equations (SDEs). In this study, two approaches, dynamic inter-occasion variability (dIOV) and adapted stochastic differential equations, were proposed to investigate WSV in pharmacometric count data analysis. These approaches were applied to published count models for seizure counts and Likert pain scores. Both approaches improved the model fits significantly. In addition, stochastic simulation and estimation were used to explore further the capability of the two approaches to diagnose and improve models where existing WSV is not recognized. The results of simulations confirmed the gain in introducing WSV as dIOV and SDEs when parameters vary randomly over time. Further, the approaches were also informative as diagnostics of model misspecification, when parameters changed systematically over time but this was not recognized in the structural model. The proposed approaches in this study offer strategies to characterize WSV and are not restricted to count data.
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Acknowledgments
The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under Grant Agreement No. 115156, resources of which are composed of financial contributions from the European Union’s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies’ in kind contribution. The DDMoRe project is also supported by financial contribution from Academic and SME partners. The authors thank Dr. Christoffer W. Tornøe for his helpful inputs of SDEs.
Author Contributions
CD., E.P. and M.K. designed the research. C.D. performed the research. C.D, E.P. and M.K. analyzed the results. C.D., E. P. and M.K. wrote the manuscript.
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Chenhui Deng was employed by Pfizer when this paper was submitted.
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Deng, C., Plan, E.L. & Karlsson, M.O. Approaches for modeling within subject variability in pharmacometric count data analysis: dynamic inter-occasion variability and stochastic differential equations. J Pharmacokinet Pharmacodyn 43, 305–314 (2016). https://doi.org/10.1007/s10928-016-9473-1
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DOI: https://doi.org/10.1007/s10928-016-9473-1