ArticleJournal of pharmacokinetics and pharmacodynamics2016
Approaches for modeling within subject variability in pharmacometric count data analysis: dynamic inter-occasion variability and stochastic differential equations.
Article in Journal of pharmacokinetics and pharmacodynamics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Assessment of components included in published societal perspective or QALY outcome economic analyses for antiepileptic drug treatment in chronic epilepsy.Expert review of pharmacoeconomics & outcomes research · 2018Pooled it
- Stochastic Differential Equations (SDEs) in NONMEM for Probing Population Pharmacokinetic Model Misspecification: Diagnostic Utility, Practical Considerations, and Future Directions.The AAPS journal · 2026Article
- A tutorial on pharmacometric Markov models.CPT: pharmacometrics & systems pharmacology · 2025Article
- Effect of Individual Patient Characteristics and Treatment Choices on Reliever Medication Use in Moderate-Severe Asthma: A Poisson Analysis of Randomised Clinical Trials.Advances in therapy · 2024Article
- Natural variability in seizure frequency: Implications for trials and placebo.Epilepsy research · 2020Article
- Handling interoccasion variability in model-based dose individualization using therapeutic drug monitoring data.British journal of clinical pharmacology · 2019Article
- Optimising infliximab induction dosing for patients with ulcerative colitis.British journal of clinical pharmacology · 2019Article
- Exact Gradients Improve Parameter Estimation in Nonlinear Mixed Effects Models with Stochastic Dynamics.The AAPS journal · 2018Article
- Pharmacometrics models with hidden Markovian dynamics.Journal of pharmacokinetics and pharmacodynamics · 2018Article
- A big data approach to the development of mixed-effects models for seizure count data.Epilepsia · 2017Article
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3 authors.
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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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