Assessing parameter uncertainty in small-n pharmacometric analyses: value of the log-likelihood profiling-based sampling importance resampling (LLP-SIR) technique

Broeker, Astrid; Wicha, Sebastian G.

doi:10.1007/s10928-020-09682-4

Cited by 19 publications

(19 citation statements)

References 15 publications

(28 reference statements)

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“…Although the bootstrap is often branded as an approach gain a good understanding of the uncertainty in small samples, Broeker and Wicha have shown that this is mostly wishful thinking as the performance in small datasets is not good [24]. We confirm this here in the two scenarios S Hill,smallR and S Hill,smallS .…”

Section: Discussionsupporting

confidence: 77%

“…With even lower number of subjects, especially in the sparse design, the bias on the parameter estimates for the asymptotic method was even more apparent (data not shown) and the bootstrap approaches could not recover from these poor estimates. With these scenarios the coverage for all bootstraps was lower than 90%, and there was no clear trend in whether the bootstraps under-or over-estimated the parameters and their SE, which is a cautionary message for the usage of bootstrap in practice as from present and previous work [15,24] the conditions in which bootstrap performances are adequate are not obvious. In our simulations, we found that the performance of the bootstraps degrade as the informativeness of the design decrease.…”

Section: Discussionmentioning

confidence: 74%

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Conditional Non-parametric Bootstrap for Non-linear Mixed Effect Models

et al. 2021

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Non-linear mixed effect models are widely used and increasingly integrated into decision-making processes. Propagating uncertainty is an important element of this process, and while standard errors (SE) on parameters are most often computed using asymptotic approaches, alternative methods such as the bootstrap are also available. In this article, we propose a modified residual parametric bootstrap taking into account the different levels of variability involved in these models.Methods: The proposed approach uses samples from the individual conditional distribution, and was implemented in R using the saemix algorithm. We performed a simulation study to assess its performance in different scenarios, comparing it to the asymptotic approximation and to standard bootstraps in terms of coverage, also looking at bias in the parameters and their SE.Results: Simulations with an Emax model with different designs and sigmoidicity factors showed a similar coverage rate to the parametric bootstrap, while requiring less hypotheses. Bootstrap improved coverage in several scenarios compared to the asymptotic method especially for the variance param-

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Section: Discussionsupporting

confidence: 77%

Section: Discussionmentioning

confidence: 74%

Conditional Non-parametric Bootstrap for Non-linear Mixed Effect Models

et al. 2021

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“…However, the concurrent use of various models will include a larger range of covariates. Third, single models developed in small, homogenous cohorts of patients while displaying good internal predictivity, might not display external predictivity due to selection bias in the covariate submodel 35 and underestimated parameter uncertainty in studies with small patient numbers 36 . Finally, single models may have been developed based upon routine clinical data, which often exhibits uncertainty in documented dosing and sampling time.…”

Section: Discussionmentioning

confidence: 99%

A Model Averaging/Selection Approach Improves the Predictive Performance of Model‐Informed Precision Dosing: Vancomycin as a Case Study

Uster

Stocker

Carland

et al. 2020

Clin Pharma and Therapeutics

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Many important drugs exhibit substantial variability in pharmacokinetics and pharmacodynamics leading to a loss of the desired clinical outcomes or significant adverse effects. Forecasting drug exposures using pharmacometric models can improve individual target attainment when compared with conventional therapeutic drug monitoring (TDM). However, selecting the 'correct' model for this model-informed precision dosing (MIPD) is challenging. We derived and evaluated a model selection algorithm (MSA) and a model averaging algorithm (MAA), which automates model selection and finds the best model or combination of models for each patient using vancomycin as a case study, and implemented both algorithms in the MIPD software 'TDMx'. The predictive performance (based on accuracy and precision) of the two algorithms was assessed in (i) a simulation study of six distinct populations and (ii) a clinical dataset of 180 patients undergoing TDM during vancomycin treatment and compared with the performance obtained using a single model. Throughout the six virtual populations the MSA and MAA (imprecision: 9.9-24.2%, inaccuracy: less than ±8.2%) displayed more accurate predictions than the single models (imprecision: 8.9-51.1%; inaccuracy: up to 28.9%).In the clinical dataset the predictive performance of the single models applying at least one plasma concentration varied substantially (imprecision: 28-62%, inaccuracy:-16-25%), while the MSA or MAA utilizing these models simultaneously, resulted in unbiased and precise predictions (imprecision: 29% and 30%, inaccuracy:-5% and 0%, respectively). MSA and MAA approaches implemented in 'TDMx' might thereby lower the burden of fit-for-purpose validation of individual models and streamline MIPD.

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“…In the clinical data examples, models were evaluated by graphical and numerical criteria (goodness of fit plots, visual predictive checks and drop in objective function value (dOFV)). The LLP-SIR (log-likelihood-profiling based sampling-importance-resampling) method was employed for the assessment of parameter uncertainty, since a small number of subjects or cases was investigated [17].…”

Section: Population Pharmacokinetic Modellingmentioning

confidence: 99%

An Integrated Dialysis Pharmacometric (IDP) Model to Evaluate the Pharmacokinetics in Patients Undergoing Renal Replacement Therapy

et al. 2020

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Purpose Clearance via renal replacement therapy (RRT) can significantly alter the pharmacokinetic profile of drugs. The aim of this study was (i) to improve the use of clinical trial data and (ii) to provide a model that allows quantification of all aspects of drug elimination via RRT including adsorption to dialysis membranes and/or degradation of the drug in the dialysate. Methods An integrated dialysis pharmacometric (IDP) model was developed to simultaneously incorporate all available RRT information. The sensitivity, accuracy and precision of the IDP model was compared to conventional approaches in clinical trial simulations and applied to clinical datasets of teicoplanin and doripenem. Results The IDP model was more accurate, precise and sensitive than conventional plasma-concentration-based approaches when estimating the clearance RRT (relative bias <1%). In contrast to conventional approaches, adsorption and degradation were quantifiable using the IDP model (relative bias: −1.1% and − 1.9%, respectively). Applied to clinical data, clearance RRT , drug degradation (effluent-half-life doripenem : 13.5 h −1 ) and adsorption (polysulphone adsorption capacity teicoplanin : 31.2 mg) were assessed. Conclusion The IDP model allows accurate, precise and sensitive characterization of clearance RRT , adsorption and degradation. Successful quantification of all aspects of clearance RRT in clinical data demonstrated the benefit of the IDP model as compared to conventional approaches. KEY WORDS adsorption . doripenem . pharmacokinetics . renal replacement therapy . teicoplanin ABBREVIATIONS CVVHD Continuous veno-venous hemodialysis CVVH Continuous veno-venous hemofiltration CVVHDF continuous veno-venous hemodiafiltration dOFV Drop in objective function value Eq. Equation IDP model Integrated dialysis pharmacometric model LLP-SIR log-likelihood-profiling based samplingimportance-resampling rBias relative Bias Astrid Broeker and Matthias G. Vossen contributed equally to this work.

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Assessing parameter uncertainty in small-n pharmacometric analyses: value of the log-likelihood profiling-based sampling importance resampling (LLP-SIR) technique

Cited by 19 publications

References 15 publications

Conditional Non-parametric Bootstrap for Non-linear Mixed Effect Models

Conditional Non-parametric Bootstrap for Non-linear Mixed Effect Models

A Model Averaging/Selection Approach Improves the Predictive Performance of Model‐Informed Precision Dosing: Vancomycin as a Case Study

An Integrated Dialysis Pharmacometric (IDP) Model to Evaluate the Pharmacokinetics in Patients Undergoing Renal Replacement Therapy

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