Confidence and Prediction Intervals for Pharmacometric Models

Kümmel, Anne; Bonate, Peter L.; Dingemanse, Jasper; Krause, Andreas

doi:10.1002/psp4.12286

Cited by 34 publications

(34 citation statements)

References 30 publications

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“…Confidence intervals from BAYES bootstrap or traditional bootstrap do not assume a multivariate normal distribution and provide a stochastic approximation of confidence interval estimation for parameters. 39 Parameters obtained using traditional bootstrap showed good concordance to parameters generated using BAYES bootstrap. An assumption made with the Monte Carlo simulationbased bootstrap is that the model (including the error model) is an accurate representation of the data.…”

Section: Discussionmentioning

confidence: 72%

“…These parameters from the Monte Carlo simulations were used to obtain summary statistics and credible intervals. Confidence intervals from BAYES bootstrap or traditional bootstrap do not assume a multivariate normal distribution and provide a stochastic approximation of confidence interval estimation for parameters . Parameters obtained using traditional bootstrap showed good concordance to parameters generated using BAYES bootstrap.…”

Section: Discussionmentioning

confidence: 95%

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Population Pharmacokinetics of Pemetrexed in Adult Non–Small Cell Lung Cancer in Indian Patients

Srinivasan

Chaturvedula

Fossler³

et al. 2019

The Journal of Clinical Pharma

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The objective of the study was to develop a population pharmacokinetic model of pemetrexed and identify factors contributing to variability in exposure in Indian patients. Plasma samples were obtained from a cohort of 85 patients following 500 mg/m2 intravenous infusion and population pharmacokinetic analysis was performed using NONMEM (version 7.3.0). The stochastic approximation expectation maximization method was used to estimate parameters. The full covariate model approach was used by specifying clinically meaningful covariates a priori. Credible intervals obtained using Markov chain Monte Carlo Bayesian analysis were used to reduce the full covariate model by eliminating the covariates whose CI included the null. Model qualification was performed using visual predictive check and bootstrap. The final population parameter estimates and relative standard error for clearance (CL) was 3.3 L/h (10.8), central volume of distribution (V1) was 5.2 L (7.8), peripheral volume of distribution (V2) was 5.9 L (14.5) and intercompartmental clearance (Q) was 6.8 L/h (14.3). A large between‐subject variability (50%‐108% coefficient of variation) was observed in pharmacokinetic parameters. The percent coefficient of variation for the area under the plasma concentration–time curve from time zero to infinity was 72% and for maximum concentration was 68.25%. Diagnostic plots showed no major bias in the model. The final model included V1, V2, and Q scaled to body surface area raised to a fixed exponent of 1. Creatinine clearance and sex on clearance and albumin on V1 were statistically significant covariates based on Bayesian credible interval. However, traditional bootstrap resulted in a 95% confidence interval of the sex effect parameter including null. Given the size and nonsignificant sex effect in traditional bootstrap, it is considered clinically not significant.

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Section: Discussionmentioning

confidence: 72%

Section: Discussionmentioning

confidence: 95%

Population Pharmacokinetics of Pemetrexed in Adult Non–Small Cell Lung Cancer in Indian Patients

Srinivasan

Chaturvedula

Fossler³

et al. 2019

The Journal of Clinical Pharma

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“…The uncertainty in the parameters is then propagated to the observables by first sampling from the normal distribution in Eq. and then solving the structural model for each sample . Alternatively to this sampling‐based approach, the Delta method (Section 5.5 in ref.…”

Section: Methodsmentioning

confidence: 99%

“…Considering a Student's t distribution instead of the normal approximation, as in ref. 13, did not lead to an adequate improvement ( Figure S3). Consequently, the NAP approach can result in overoptimistic, overpessimistic, and unrealistic predictions.…”

Section: Approximation Accuracies Comparable Across Different Full Bamentioning

confidence: 97%

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Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy

Maier

Hartung

Wiljes

et al. 2020

CPT Pharmacom & Syst Pharma

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An essential component of therapeutic drug/biomarker monitoring (TDM) is to combine patient data with prior knowledge for model‐based predictions of therapy outcomes. Current Bayesian forecasting tools typically rely only on the most probable model parameters (maximum a posteriori (MAP) estimate). This MAP‐based approach, however, does neither necessarily predict the most probable outcome nor does it quantify the risks of treatment inefficacy or toxicity. Bayesian data assimilation (DA) methods overcome these limitations by providing a comprehensive uncertainty quantification. We compare DA methods with MAP‐based approaches and show how probabilistic statements about key markers related to chemotherapy‐induced neutropenia can be leveraged for more informative decision support in individualized chemotherapy. Sequential Bayesian DA proved to be most computationally efficient for handling interoccasion variability and integrating TDM data. For new digital monitoring devices enabling more frequent data collection, these features will be of critical importance to improve patient care decisions in various therapeutic areas.

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Propagating input uncertainties into parameter uncertainties and model prediction uncertainties—A review

2023

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A review of uncertainty quantification techniques is provided for a variety of situations involving uncertainties in model inputs (independent variables). The situations of interest are divided into three categories: (i) when model prediction uncertainties are quantified based on uncertainties in uncertain inputs, (ii) when parameter estimate uncertainties are calculated by propagation of uncertainties from measured inputs and outputs, and (iii) when model prediction uncertainties are quantified based on corresponding uncertainties in measured inputs and uncertain parameter estimates. For all three situations, linearization‐based and Monte Carlo‐based techniques are reviewed and details for their corresponding algorithms are presented. Recommendations are provided on which uncertainty quantification techniques are best for different types of chemical engineering models based on the amount of input uncertainty and nonlinearity over the range of plausible input and parameter values.

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Confidence and Prediction Intervals for Pharmacometric Models

Cited by 34 publications

References 30 publications

Population Pharmacokinetics of Pemetrexed in Adult Non–Small Cell Lung Cancer in Indian Patients

Population Pharmacokinetics of Pemetrexed in Adult Non–Small Cell Lung Cancer in Indian Patients

Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy

Propagating input uncertainties into parameter uncertainties and model prediction uncertainties—A review

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