scholarly journals Using Akaike's information theoretic criterion in mixed-effects modeling of pharmacokinetic data: a simulation study

F1000Research ◽  
2014 ◽  
Vol 2 ◽  
pp. 71 ◽  
Author(s):  
Erik Olofsen ◽  
Albert Dahan
Keyword(s):  
Simulation Study ◽  
Prediction Error ◽  
Interindividual Variability ◽  
Predictive Performance ◽  
Performance Measure ◽  
Mixed Effects ◽  
Pharmacokinetic Data ◽  
Information Theoretic ◽  
Correct Model ◽  
Drug Concentrations

Akaike's information theoretic criterion for model discrimination (AIC) is often stated to "overfit", i.e., it selects models with a higher dimension than the dimension of the model that generated the data. However, with experimental pharmacokinetic data it may not be possible to identify the correct model, because of the complexity of the processes governing drug disposition. Instead of trying to find the correct model, a more useful objective might be to minimize the prediction error of drug concentrations in subjects with unknown disposition characteristics. In that case, the AIC might be the selection criterion of choice.We performed Monte Carlo simulations using a model of pharmacokinetic data (a power function of time) with the property that fits with common multi-exponential models can never be perfect - thus resembling the situation with real data. Prespecified models were fitted to simulated data sets, and AIC and AICc (the criterion with a correction for small sample sizes) values were calculated and averaged. The average predictive performances of the models, quantified using simulated validation sets, were compared to the means of the AICs. The data for fits and validation consisted of 11 concentration measurements each obtained in 5 individuals, with three degrees of interindividual variability in the pharmacokinetic volume of distribution.Mean AICc corresponded very well, and better than mean AIC, with mean predictive performance. With increasing interindividual variability, there was a trend towards larger optimal models, but with respect to both lowest AICc and best predictive performance. Furthermore, it was observed that the mean square prediction error itself became less suitable as a validation criterion, and that a predictive performance measure should incorporate interindividual variability.This simulation study showed that, at least in a relatively simple mixed effects modelling context with a set of prespecified models, minimal mean AICc corresponded to best predictive performance even in the presence of relatively large interindividual variability.

scholarly journals Using Akaike's information theoretic criterion in mixed-effects modeling of pharmacokinetic data: a simulation study

F1000Research ◽  
2015 ◽  
Vol 2 ◽  
pp. 71 ◽  
Author(s):  
Erik Olofsen ◽  
Albert Dahan
Keyword(s):  
Simulation Study ◽  
Prediction Error ◽  
Interindividual Variability ◽  
Predictive Performance ◽  
Performance Measure ◽  
Mixed Effects ◽  
Pharmacokinetic Data ◽  
Information Theoretic ◽  
Correct Model ◽  
Drug Concentrations

Akaike's information theoretic criterion for model discrimination (AIC) is often stated to "overfit", i.e., it selects models with a higher dimension than the dimension of the model that generated the data. However, with experimental pharmacokinetic data it may not be possible to identify the correct model, because of the complexity of the processes governing drug disposition. Instead of trying to find the correct model, a more useful objective might be to minimize the prediction error of drug concentrations in subjects with unknown disposition characteristics. In that case, the AIC might be the selection criterion of choice.We performed Monte Carlo simulations using a model of pharmacokinetic data (a power function of time) with the property that fits with common multi-exponential models can never be perfect - thus resembling the situation with real data. Prespecified models were fitted to simulated data sets, and AIC and AICc (the criterion with a correction for small sample sizes) values were calculated and averaged. The average predictive performances of the models, quantified using simulated validation sets, were compared to the means of the AICs. The data for fits and validation consisted of 11 concentration measurements each obtained in 5 individuals, with three degrees of interindividual variability in the pharmacokinetic volume of distribution.Mean AICc corresponded very well, and better than mean AIC, with mean predictive performance. With increasing interindividual variability, there was a trend towards larger optimal models, but with respect to both lowest AICc and best predictive performance. Furthermore, it was observed that the mean square prediction error itself became less suitable as a validation criterion, and that a predictive performance measure should incorporate interindividual variability.This simulation study showed that, at least in a relatively simple mixed-effects modelling context with a set of prespecified models, minimal mean AICc corresponded to best predictive performance even in the presence of relatively large interindividual variability.

scholarly journals Using Akaike’s information theoretic criterion in population analysis: a simulation study

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 71 ◽  
Author(s):  
Erik Olofsen ◽  
Albert Dahan
Keyword(s):  
Simulation Study ◽  
Interindividual Variability ◽  
Population Analysis ◽  
Selection Criterion ◽  
Population Data ◽  
Predictive Performance ◽  
Pharmacokinetic Data ◽  
Information Theoretic ◽  
Mixed Effects Modeling ◽  
Present Simulation

Akaike’s information-theoretic criterion for model discrimination (AIC) is often stated to “overfit”, i.e., it selects models with a higher dimension than the dimension of the model that generated the data. However, when no fixed-dimensional correct model exists, for example for pharmacokinetic data, AIC, or its bias-corrected version (AICc) might be the selection criterion of choice if the objective is to minimize prediction error. The present simulation study was designed to assess the behavior of AICc when applying it to the analysis of population data, for various degrees of interindividual variability. The simulation study showed that, at least in a relatively simple mixed effects modeling context, minimal mean AICc corresponded to best predictive performance even in the presence of large interindividual variability.

and σ +σ { , respectively, the KL } D ( for PBE ) is 111d(f ) = (µ −µ ) + − 2. (7.17) 2 σ For IBE, the { KLD is 1 }( ) d(f ) = (µ +σ + 2 − 2, 2 σ σWR (7.18) where σ = Var(s ) = σ − 2ρσ σBR . Advantages of using the KLD are that it: (1) possesses a natural hi-erarchical property such that IBE implies PBE and PBE implies ABE, (2) satisfies the properties of a true distance metric, (3) is invariant to monotonic transformations of the data, (4) generalizes easily to the mul-tivariate case where equivalence on more than one parameter (e.g., AUC, Cmax and Tmax) is required and (5) is applicable over a wide range of distributions of the response variable (e.g., those in the exponential fam-ily). Patterson et al. (2001) and Dragalin et al. (2002), described the results of a simulation study and a retrospective analysis of 22 replicate design datasets, to compare testing for IBE using the KLD with testing based on the FDA-recommended metric defined earlier in Section 7.4. One notable finding of these studies was that the KLD metric identified more datasets as being of concern than the FDA-recommended metric. This appeared to be due to ability of the FDA-recommended metric to reward novel formulations for which the within-subject variance is decreased relative to the reference. 7.8 Modelling pharmacokinetic data Although AUC and Cmax are adequate for testing for bioequivalence, there is sometimes a need to model the drug concentrations over time. Fitting such models aids the understanding of how the drug is absorbed and eliminated from the body, as well as allowing model-based estimates of AUC and Cmax to be obtained. A popular type of model that is fitted in these circumstances is the compartmental model, which considers the body as made up of a number of compartments through which the drug circulates. For example, the circulating blood might be considered as the single compartment in a one-compartment model. If a drug is taken orally as a tablet, say, the drug is absorbed into this compartment as the tablet dissolves in the stomach and is eliminated from this compartment by (among other things) the actions of the liver and kidneys. While the tablet is still being dissolved in the stomach, the rate of absorption of the drug into the circulating blood is greater than the rate that is eliminated

2003 ◽  
pp. 372-372
Keyword(s):  
Simulation Study ◽  
Compartment Model ◽  
The Body ◽  
Pharmacokinetic Data ◽  
Response Variable ◽  
True Distance ◽  
Drug Concentrations ◽  
Wide Range ◽  
Rate Of Absorption ◽  
Over Time

scholarly journals Information-Theoretic Generalization Bounds for Meta-Learning and Applications

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 126
Author(s):  
Sharu Theresa Jose ◽  
Osvaldo Simeone
Keyword(s):  
Learning Algorithm ◽  
Broad Class ◽  
Performance Measure ◽  
Training Data ◽  
Learning To Learn ◽  
Data Set ◽  
Information Theoretic ◽  
Meta Learning ◽  
Task Training ◽  
Test Sets

Meta-learning, or “learning to learn”, refers to techniques that infer an inductive bias from data corresponding to multiple related tasks with the goal of improving the sample efficiency for new, previously unobserved, tasks. A key performance measure for meta-learning is the meta-generalization gap, that is, the difference between the average loss measured on the meta-training data and on a new, randomly selected task. This paper presents novel information-theoretic upper bounds on the meta-generalization gap. Two broad classes of meta-learning algorithms are considered that use either separate within-task training and test sets, like model agnostic meta-learning (MAML), or joint within-task training and test sets, like reptile. Extending the existing work for conventional learning, an upper bound on the meta-generalization gap is derived for the former class that depends on the mutual information (MI) between the output of the meta-learning algorithm and its input meta-training data. For the latter, the derived bound includes an additional MI between the output of the per-task learning procedure and corresponding data set to capture within-task uncertainty. Tighter bounds are then developed for the two classes via novel individual task MI (ITMI) bounds. Applications of the derived bounds are finally discussed, including a broad class of noisy iterative algorithms for meta-learning.

Entropy Production as a Predictive Performance Measure: I — Turbomachinery

1999 ◽  
Author(s):  
David M. Paulus ◽  
Richard A. Gaggioli ◽  
William R. Dunbar
Keyword(s):  
Mathematical Modeling ◽  
Energy Conversion ◽  
Entropy Production ◽  
Predictive Performance ◽  
Performance Measure ◽  
Governing Equations ◽  
Compressor Performance

Abstract It is proposed that consideration be given to an alternative, streamlined manner for mathematical modeling of the performance of energy conversion and transfer equipment. We make the case, here, by application to compressors. It is advocated that, instead of using an expression for efficiency as one of the governing equations, performance can be accounted for directly, with entropy production. It is shown that (1) the modeling is more straightforward, using fewer relations, and (2) that compressor performance (e.g. maps) can be represented equally well.

scholarly journals Predicting absorption and pharmacokinetic profile of carbamazepine from controlled-release tablet formulation in humans using rabbit model

2011 ◽  
Vol 65 (1-2) ◽  
pp. 71-81
Author(s):  
Irena Homsek ◽  
Dragica Popadic ◽  
Slobodanka Simic ◽  
Slavica Ristic ◽  
Katarina Vucicevic ◽  
...  
Keyword(s):  
Controlled Release ◽  
Rabbit Model ◽  
Tablet Formulation ◽  
Human Model ◽  
In Vitro Dissolution ◽  
Pharmacokinetic Parameters ◽  
Pharmacokinetic Data ◽  
Drug Concentrations

Controlled-release (CR) pharmaceutical formulations offer several advantages over the conventional, immediate release dosage forms of the same drug, including reduced dosing frequency, decreased incidence and/or intensity of adverse effects, greater selectivity of pharmacological activity, reduced drug plasma fluctuation, and better compliance. After a drug product has been registered, and is already on market, minor changes in formulation might be needed. At the same time, the product has to remain effective and safe for patients that could be confirmed via plasma drug concentrations and pharmacokinetic characteristics. It is challenging to predict human absorption and pharmacokinetic characteristics of a drug based on the in vitro dissolution test and the animal pharmacokinetic data. Therefore, the objective of this study was to establish correlation of the pharmacokinetic parameters of carbamazepine (CBZ) CR tablet formulation between the rabbit and the human model, and to establish in vitro in vivo correlation (IVIVC) based on the predicted fractions of absorbed CBZ. Although differences in mean plasma concentration profiles were notified, the data concerning the predicted fraction of drug absorbed were almost superimposable. Accordingly, it can be concluded that rabbits may be representative as an in vivo model for predicting the pharmacokinetics of the CR formulation of CBZ in humans.

scholarly journals Comparison of Statistical Methods for Estimating Continuous Paediatric Reference Intervals: A Simulation Study

2021 ◽  
Author(s):  
Monsurul Hoq ◽  
Susan Donath ◽  
Paul Monagle ◽  
John Carlin
Keyword(s):  
Statistical Methods ◽  
Simulation Study ◽  
Assessment Tool ◽  
Polynomial Regression ◽  
Reference Intervals ◽  
Performance Measure ◽  
Parametric Models ◽  
New Method ◽  
Sample Sizes ◽  
Generalised Additive Model

Abstract Background: Reference intervals (RIs), which are used as an assessment tool in laboratory medicine, change with age for most biomarkers in children. Addressing this, RIs that vary continuously with age have been developed using a range of curve-fitting approaches. The choice of statistical method may be important as different methods may produce substantially different RIs. Hence, we developed a simulation study to investigate the performance of statistical methods for estimating continuous paediatric RIs.Methods: We compared four methods for estimating age-varying RIs. These were Cole’s LMS, the Generalised Additive Model for Location Scale and Shape (GAMLSS), Royston’s method based on fractional polynomials and exponential transformation, and a new method applying quantile regression using power variables in age selected by fractional polynomial regression for the mean. Data were generated using hypothetical true curves based on five biomarkers with varying complexity of association with age, i.e. linear or nonlinear, constant or nonconstant variation across age, and for four sample sizes (100, 200, 400 and 1000). Root mean square error (RMSE) was used as the primary performance measure for comparison. Results: Regression-based parametric methods performed better in most scenarios. Royston’s and the new method performed consistently well in all scenarios for sample sizes of at least 400, while the new method had the smallest average RMSE in scenarios with nonconstant variation across age. Conclusions: We recommend methods based on flexible parametric models for estimating continuous paediatric RIs, irrespective of the complexity of the association between biomarkers and age, for at least 400 samples.

Optimising pyrazinamide for the treatment of tuberculosis

2021 ◽  
pp. 2002013
Author(s):  
Nan Zhang ◽  
Radojka M. Savic ◽  
Martin J. Boeree ◽  
Charles Peloquin ◽  
Marc Weiner ◽  
...  
Keyword(s):  
Treatment Duration ◽  
Liver Toxicity ◽  
Liver Function Tests ◽  
P Value ◽  
Pharmacokinetic Data ◽  
Drug Concentrations ◽  
High Doses ◽  
Grade 3 ◽  
Parametric Survival ◽  
Culture Conversion

Pyrazinamide is a potent sterilising agent that shortens the treatment duration needed to cure tuberculosis. It is synergistic with novel and existing drugs for tuberculosis. The dose of pyrazinamide that optimises efficacy while remaining safe is uncertain, as is its potential role in shortening treatment duration further.Pharmacokinetic data, sputum culture, and safety laboratory results were compiled from TBTC Studies 27 and 28 and PanACEA MAMS-TB, multi-center Phase 2 trials in which participants received rifampicin (range 10–35 mg·kg−1), pyrazinamide (range 20–30 mg·kg−1), plus two companion drugs. Pyrazinamide pharmacokinetic-pharmacodynamic (PK/PD) and PK-toxicity analyses were performed.In TBTC studies (n=77), higher pyrazinamide maximum concentration (Cmax) was associated with shorter time to culture conversion (TTCC) and higher probability of two-month culture conversion (p-value<0.001). Parametric survival analyses showed that relationships varied geographically, with steeper PK-PD relationships seen among non-African than African participants. In PanACEA MAMS-TB (n=363), TTCC decreased as pyrazinamide Cmax increased and varied by rifampicin Cmax (p-value<0.01). Modeling and simulation suggested that very high doses of pyrazinamide (>4500 mg) or increasing both pyrazinamide and rifampicin would be required to reach targets associated with treatment shortening. Combining all trials, liver toxicity was rare (3.9% with Grade 3 or higher liver function tests, LFT), and no relationship was seen between pyrazinamide Cmax and LFT levels.Pyrazinamide's microbiologic efficacy increases with increasing drug concentrations. Optimising pyrazinamide alone, though, is unlikely to be sufficient to allow tuberculosis treatment shortening; rather, rifampicin dose would need to be increased in parallel.

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