Parametric and Nonparametric Population Methods

Bustad, Aida; Terziivanov, D; Leary, Robert; Port, Ruediger E.; Schumitzky, Alan; Jelliffe, Roger W.

doi:10.2165/00003088-200645040-00003

Cited by 77 publications

(20 citation statements)

References 14 publications

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“…This means that with the given data, the estimations of the parameters are those that occur with the highest probability. Alternative software packages that can be used are, for example, Monolix, WinNonMix, USC*PAC, which uses non-parametric maximum likelihood methods [44], or ADAPT, using maximum a posteriori (MAP) methods [45]. …”

Section: Methods Of Analysing Data: Standard Two-stage or Population mentioning

confidence: 99%

The role of population PK–PD modelling in paediatric clinical research

Cock

Piana

Krekels

et al. 2010

Eur J Clin Pharmacol

192

177

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Children differ from adults in their response to drugs. While this may be the result of changes in dose exposure (pharmacokinetics [PK]) and/or exposure response (pharmacodynamics [PD]) relationships, the magnitude of these changes may not be solely reflected by differences in body weight. As a consequence, dosing recommendations empirically derived from adults dosing regimens using linear extrapolations based on body weight, can result in therapeutic failure, occurrence of adverse effect or even fatalities. In order to define rational, patient-tailored dosing schemes, population PK–PD studies in children are needed. For the analysis of the data, population modelling using non-linear mixed effect modelling is the preferred tool since this approach allows for the analysis of sparse and unbalanced datasets. Additionally, it permits the exploration of the influence of different covariates such as body weight and age to explain the variability in drug response. Finally, using this approach, these PK–PD studies can be designed in the most efficient manner in order to obtain the maximum information on the PK–PD parameters with the highest precision. Once a population PK–PD model is developed, internal and external validations should be performed. If the model performs well in these validation procedures, model simulations can be used to define a dosing regimen, which in turn needs to be tested and challenged in a prospective clinical trial. This methodology will improve the efficacy/safety balance of dosing guidelines, which will be of benefit to the individual child.

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Section: Methods Of Analysing Data: Standard Two-stage or Population mentioning

confidence: 99%

The role of population PK–PD modelling in paediatric clinical research

Cock

Piana

Krekels

et al. 2010

Eur J Clin Pharmacol

192

177

View full text Add to dashboard Cite

show abstract

“…This most desirable property of statistical consistency means that the more subjects one studies in the population, the closer the estimated parameter distributions approach the true ones. This means that the more subjects studied, the closer the predicted parameter value approaches the true value [9, 12]. …”

Section: Methodsmentioning

confidence: 99%

“…The objective of the present study was to analyze a representative population of critically ill Kuwaiti patients receiving amikacin therapy using the nonparametric adaptive grid (NPAG) program, in the MM-USCPACK collection [8, 9]. This software has been incorporated into the Pmetrics software, which is now embedded in R.…”

Section: Introductionmentioning

confidence: 99%

Amikacin Population Pharmacokinetics in Critically Ill Kuwaiti Patients

Matar

Al-Lanqawi

Abdulmalek

et al. 2013

BioMed Research International

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Amikacin pharmacokinetic data in Kuwaiti (Arab) intensive care unit (ICU) patients are lacking. Fairly sparse serum amikacin peak and trough concentrations data were obtained from adult Kuwaiti ICU patients. The data were analysed using a nonparametric adaptive grid (NPAG) maximum likelihood algorithm. The estimations of the developed model were assessed using mean error (ME) as a measure of bias and mean squared error (MSE) as a measure of precision. A total of 331 serum amikacin concentrations were obtained from 56 patients. The mean (±SD) model parameter values found were V c = 0.2302 ± 0.0866 L/kg, k slope = 0.004045 ± 0.00705 min per unit of creatinine clearance, k 12 = 2.2121 ± 5.506 h−1, and k 21 = 1.431 ± 2.796 h−1. The serum concentration data were estimated with little bias (ME = −0.88) and good precision (MSE = 13.08). The present study suggests that amikacin pharmacokinetics in adult Kuwaiti ICU patients are generally rather similar to those found in other patients. This population model would provide useful guidance in developing initial amikacin dosage regimens for such patients, especially using multiple model (MM) dosage design, followed by appropriate Bayesian adaptive control, to optimize amikacin dosage regimens for each individual patient.

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“…While we have an IT erative 2 -stage B ayesian (IT2B) parametric method, for many years our focus has been on non-parametric methods. We started with the N on- P arametric E xpectation M aximization (NPEM) algorithm, 4 and moved to a combination of NPEM for the first cycle of the model fitting process, followed by cycles that use our N on- P arametric A daptive G rid (NPAG) algorithm of Leary and Burke (described by Baek 5 and Bustad et al, 6 with a definitive description now submitted elsewhere for publication by Yamada et al).…”

Section: Introductionmentioning

confidence: 99%

Accurate Detection of Outliers and Subpopulations With Pmetrics, a Nonparametric and Parametric Pharmacometric Modeling and Simulation Package for R

Neely¹,

Guilder²,

Yamada³

et al. 2012

Therapeutic Drug Monitoring

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373

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Introduction Non-parametric population modeling algorithms have a theoretical superiority over parametric methods to detect pharmacokinetic and pharmacodynamic sub-groups and outliers within a study population. Methods The authors created “Pmetrics”, a new Windows and Unix R software package that updates the older MM-USCPACK software for non-parametric and parametric population modeling and simulation of pharmacokinetic and pharmacodynamic systems. The parametric iterative two-stage Bayesian (IT2B) and the non-parametric adaptive grid (NPAG) approaches in Pmetrics were used to fit a simulated population with bimodal elimination (Kel) and unimodal volume of distribution (Vd), plus an extreme outlier, for a one-compartment model of an intravenous drug. Results The true means (SD) for Kel and Vd in the population sample were 0.19 (0.17) and 102 (22.3), respectively. Those found by NPAG were 0.19 (0.16) and 104 (22.6). IT2B estimated them to be 0.18 (0.16) and 104 (24.4). However, given the bimodality of Kel, no subject had a value near the mean for the population. Only NPAG was able to accurately detect the bimodal distribution for Kel and to find the outlier in both the population model and in the Bayesian posterior parameter estimates. Conclusion Built on over three decades of work, Pmetrics contains a robust, reliable, and mature non-parametric approach to population modeling, which was better than the parametric method at discovering true pharmacokinetic sub-groups and an outlier.

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Parametric and Nonparametric Population Methods

Cited by 77 publications

References 14 publications

The role of population PK–PD modelling in paediatric clinical research

The role of population PK–PD modelling in paediatric clinical research

Amikacin Population Pharmacokinetics in Critically Ill Kuwaiti Patients

Accurate Detection of Outliers and Subpopulations With Pmetrics, a Nonparametric and Parametric Pharmacometric Modeling and Simulation Package for R

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