This study aimed to investigate the relationship between gene polymorphisms and clinical factors with the concentrations of valproic acid (VPA) in adult patients who underwent neurosurgery in China.Ī total of 531 serum concentration samples at steady state were collected from 313 patients to develop a population pharmacokinetic (PPK) model. Sample size estimates showed that to compare lipoprotein secretion the mixed effects approach needed almost half the sample size as the traditional method. We conclude that the mixed effects approach provided better estimates using the full data set as well as with both sparse and truncated data sets. We compare the traditional and the mixed effects approach in terms of group estimates at various sample and data set sizes. We developed a mixed effects model to study lipoprotein kinetics in a data set of 15 healthy individuals and 15 patients with type 2 diabetes. By the use of mathematical models and tracer experiments fluxes and production rates of lipoproteins may be estimated. Lipoproteins and plasma lipids are key mediators for cardiovascular disease in metabolic disorders such as diabetes mellitus type 2. Mixed effects models offer a tool to simultaneously assess individual and population behavior from experimental data. The variability in such systems makes it difficult to translate individual characteristics to group behavior. Mathematical models may help the analysis of biological systems by providing estimates of otherwise un-measurable quantities such as concentrations and fluxes. Simulation of clinical trial suggests patient recruitment using the information of precise disease time of each patient will decrease the sample size required for clinical trials. The covariate analysis revealed earlier onset of amyloid‐β accumulation in male and female apolipoprotein E ε4 homozygotes, whereas disease progression was remarkably slower in female ε3 homozygotes compared to female ε4 carriers and males. Application of this method to sporadic Alzheimer's disease successfully depicted disease progression over 20 years. We developed a novel method to reconstitute long‐term disease progression from temporally fragmented data by extending the nonlinear mixed‐effects model to incorporate the estimation of “disease time” of each subject. Therefore, obtaining a quantitative and comprehensive understanding of the chronology of chronic diseases is challenging, due to the inability to precisely estimate the patient's disease stage at the time‐point of observation. These examples demonstrate that stochastic differential mixed effects models are useful tools for identifying incomplete or inaccurate model dynamics and for reducing potential bias in parameter estimates due to such model deficiencies.Ĭlinical observations of patients with chronic diseases are often restricted in terms of duration. Discrepancies between model predictions and observations, previously described as measurement noise only, are now separated into a comparatively lower level of measurement noise and a significant uncertainty in model dynamics. The parameter estimates are compared between a deterministic and a stochastic NiAc disposition model, respectively. Second, we consider an extension to a stochastic pharmacokinetic model in a preclinical study of nicotinic acid kinetics in obese Zucker rats. If the stochastic part is neglected, the parameter estimates become biased, and the measurement error variance is significantly overestimated. We show that by using the proposed method, the three sources of variability can be successfully separated. First, we use a stochastic one-compartmental model with first-order input and nonlinear elimination to generate synthetic data in a simulated study. To illustrate the application of the stochastic differential mixed effects model, two pharmacokinetic models are considered. The approximate population likelihood is derived using the first-order conditional estimation with interaction method and extended Kalman filtering. To this end, we extend the ordinary differential equation setting used in nonlinear mixed effects models to include stochastic differential equations. In addition to the two commonly encountered sources, measurement error and interindividual variability, we also consider uncertainty in the dynamical model itself. Inclusion of stochastic differential equations in mixed effects models provides means to quantify and distinguish three sources of variability in data.
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