Bootstrap approach for constructing confidence intervals for population pharmacokinetic parameters. I: a use of bootstrap standard error

Yafune, Akifumi; Ishiguro, Makio

doi:10.1002/(sici)1097-0258(19990315)18:5<581::aid-sim47>3.0.co;2-1

Cited by 95 publications

(50 citation statements)

References 18 publications

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“…The fact that the uncertainty based on the covariance matrix was suboptimal for some parameters, especially at low samples sizes, was not surprising. Such behaviour had been previously observed, for example with simulations with a two-compartment PK model of a dataset comprising 26 individuals and rather rich sampling where the 95 % CI did not include the true simulation value for 7 out of the 12 model parameters [36]. …”

Section: Discussionmentioning

confidence: 63%

Improving the estimation of parameter uncertainty distributions in nonlinear mixed effects models using sampling importance resampling

Dosne

Bergstrand

Harling

et al. 2016

J Pharmacokinet Pharmacodyn

197

196

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Taking parameter uncertainty into account is key to make drug development decisions such as testing whether trial endpoints meet defined criteria. Currently used methods for assessing parameter uncertainty in NLMEM have limitations, and there is a lack of diagnostics for when these limitations occur. In this work, a method based on sampling importance resampling (SIR) is proposed, which has the advantage of being free of distributional assumptions and does not require repeated parameter estimation. To perform SIR, a high number of parameter vectors are simulated from a given proposal uncertainty distribution. Their likelihood given the true uncertainty is then approximated by the ratio between the likelihood of the data given each vector and the likelihood of each vector given the proposal distribution, called the importance ratio. Non-parametric uncertainty distributions are obtained by resampling parameter vectors according to probabilities proportional to their importance ratios. Two simulation examples and three real data examples were used to define how SIR should be performed with NLMEM and to investigate the performance of the method. The simulation examples showed that SIR was able to recover the true parameter uncertainty. The real data examples showed that parameter 95 % confidence intervals (CI) obtained with SIR, the covariance matrix, bootstrap and log-likelihood profiling were generally in agreement when 95 % CI were symmetric. For parameters showing asymmetric 95 % CI, SIR 95 % CI provided a close agreement with log-likelihood profiling but often differed from bootstrap 95 % CI which had been shown to be suboptimal for the chosen examples. This work also provides guidance towards the SIR workflow, i.e.,which proposal distribution to choose and how many parameter vectors to sample when performing SIR, using diagnostics developed for this purpose. SIR is a promising approach for assessing parameter uncertainty as it is applicable in many situations where other methods for assessing parameter uncertainty fail, such as in the presence of small datasets, highly nonlinear models or meta-analysis.Electronic supplementary materialThe online version of this article (doi:10.1007/s10928-016-9487-8) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 63%

Improving the estimation of parameter uncertainty distributions in nonlinear mixed effects models using sampling importance resampling

Dosne

Bergstrand

Harling

et al. 2016

J Pharmacokinet Pharmacodyn

197

196

View full text Add to dashboard Cite

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“…1.00-500 ng/mL for the medium range method (precision: 2.5-3.1%; accuracy: −1.5 to 0.0%). A nonparametric unstratified bootstrap 18 was conducted on the PPK model using the full dataset to determine the confidence intervals of the parameters for the final models. Parameters were evaluated to ensure the 95% confidence intervals did not include zero.…”

Section: Bioanalytical Methodsmentioning

confidence: 99%

Population pharmacokinetics of pevonedistat alone or in combination with standard of care in patients with solid tumours or haematological malignancies

Faessel

Mould

Zhou

et al. 2019

Brit J Clinical Pharma

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AimsA population pharmacokinetic (PK) analysis was conducted to quantify the impact of patient‐specific and concurrent medication factors on pevonedistat PK.MethodsData were pooled from 6 clinical studies consisting of 335 patients with solid tumours or haematological malignancies administered pevonedistat alone or in combination with azacitidine, docetaxel, carboplatin + paclitaxel, or gemcitabine. Model development and covariate analysis followed standard methods. Parameters and bootstrap 95% confidence intervals were estimated using nonlinear mixed‐effects modelling. The final model was evaluated using visual predictive checks and other goodness‐of‐fit criteria.ResultsA linear 2‐compartment model best described pevonedistat PK. The final model included the effect of body surface area (BSA) on clearance (CL and Q) and volume of distribution of pevonedistat, effect of concomitantly administered carboplatin + paclitaxel on CL, and effect of albumin on Q. Race, sex, age, tumour type (haematological vs solid), mild or moderate renal impairment (creatinine clearance ≥30 mL/min), or mild hepatic impairment, had no impact on pevonedistat PK.ConclusionsThe clinical PK profile of pevonedistat is comparable in patients with solid tumours or haematological malignancies. All PK parameters exhibited ≥20% change over the observed BSA range (1.38–3 m2) with CL ranging from 75.5 to 208% of the reference value, with simulations supporting BSA‐based dosing to minimize interindividual variability in drug exposures. Concurrent administration of carboplatin + paclitaxel decreased pevonedistat CL by approximately 44%, while coadministration with azacitidine, gemcitabine or docetaxel did not alter pevonedistat CL. No other factors were identified as influencing pevonedistat PK.

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“…Nonparametric bootstrap method using Phoenix was used to evaluate the precision of parameter estimation in the final model 24,11 . 200 replications were generated by re-sampling from the observed morphine concentration dataset and PK parameters were estimated for each replicates separately.…”

Section: Methodsmentioning

confidence: 99%

Mechanistic Population Pharmacokinetics of Morphine in Neonates With Abstinence Syndrome After Oral Administration of Diluted Tincture of Opium

Liu

Lewis

Gauda

et al. 2016

The Journal of Clinical Pharma

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Conducting and analyzing clinical trials in vulnerable neonates are extremely challenging. The aim of this analysis is to develop a morphine population pharmacokinetics (PK) model using data collected during a randomized control trial in neonates with Abstinence Syndrome (NAS). Three compartment morphine structural PK model after intravenous (IV) administration from previously published work was utilized as prior, while allometric scaling method with physiological consideration was used to extrapolate PK profile from adults to pediatrics. Absorption rate constant and bioavailability were estimated in neonates with abstinence syndrome after oral administration of diluted tincture of opium (DTO). Goodness-of fit plots along with normalized prediction distribution error and bootstrap method were performed for model evaluation. We successfully extrapolated the PK profile from adults to pediatrics after IV administration. The estimated first-order absorption rate constant and bioavailability were 0.751 hour -1 and 48.5%, respectively. Model evaluations showed that the model can accurately and precisely describe the observed data. The population pharmacokinetic model we derived for morphine after oral administration of DTO is reasonable and acceptable; therefore, it can be used to describe the PK and guide future studies. The integration of the previous population PK knowledge as prior information successfully overcomes the logistic and practical issue in vulnerable neonate population.

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Bootstrap approach for constructing confidence intervals for population pharmacokinetic parameters. I: a use of bootstrap standard error

Cited by 95 publications

References 18 publications

Improving the estimation of parameter uncertainty distributions in nonlinear mixed effects models using sampling importance resampling

Improving the estimation of parameter uncertainty distributions in nonlinear mixed effects models using sampling importance resampling

Population pharmacokinetics of pevonedistat alone or in combination with standard of care in patients with solid tumours or haematological malignancies

Mechanistic Population Pharmacokinetics of Morphine in Neonates With Abstinence Syndrome After Oral Administration of Diluted Tincture of Opium

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