| 1. |
- Baverel, Paul
(författare)
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Development and Evaluation of Nonparametric Mixed Effects Models
- 2011
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A nonparametric population approach is now accessible to a more comprehensive network of modelers given its recent implementation into the popular NONMEM application, previously limited in scope by standard parametric approaches for the analysis of pharmacokinetic and pharmacodynamic data. The aim of this thesis was to assess the relative merits and downsides of nonparametric models in a nonlinear mixed effects framework in comparison with a set of parametric models developed in NONMEM based on real datasets and when applied to simple experimental settings, and to develop new diagnostic tools adapted to nonparametric models. Nonparametric models as implemented in NONMEM VI showed better overall simulation properties and predictive performance than standard parametric models, with significantly less bias and imprecision in outcomes of numerical predictive check (NPC) from 25 real data designs. This evaluation was carried on by a simulation study comparing the relative predictive performance of nonparametric and parametric models across three different validation procedures assessed by NPC. The usefulness of a nonparametric estimation step in diagnosing distributional assumption of parameters was then demonstrated through the development and the application of two bootstrapping techniques aiming to estimate imprecision of nonparametric parameter distributions. Finally, a novel covariate modeling approach intended for nonparametric models was developed with good statistical properties for identification of predictive covariates. In conclusion, by relaxing the classical normality assumption in the distribution of model parameters and given the set of diagnostic tools developed, the nonparametric approach in NONMEM constitutes an attractive alternative to the routinely used parametric approach and an improvement for efficient data analysis.
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| 2. |
- Baverel, Paul, et al.
(författare)
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Evaluation of the Nonparametric Estimation Method in NONMEM VI: Application to Real Data
- 2009
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 36:4, s. 297-315
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Tidskriftsartikel (refereegranskat)abstract
- The aim of the study was to evaluate the nonparametric estimation methods available in NONMEM VI in comparison with the parametric first-order method (FO) and the first-order conditional estimation method (FOCE) when applied to real datasets. Four methods for estimating model parameters and parameter distributions (FO, FOCE, nonparametric preceded by FO (FO-NONP) and nonparametric preceded by FOCE (FOCE-NONP)) were compared for 25 models previously developed using real data and a parametric method. Numerical predictive checks were used to test the appropriateness of each model. Up to 1000 new datasets were simulated from each model and with each method to construct 90% and 50% prediction intervals. The mean absolute error and the mean error of the different outcomes investigated were computed as indicators of imprecision and bias respectively and formal statistical tests were performed. Overall, less imprecision and less bias were observed with nonparametric methods than with parametric methods. Across the 25 models, t-tests revealed that imprecision and bias were significantly lower (P < 0.05) with FOCE-NONP than with FOCE for half of the NPC outcomes investigated. Improvements were even more pronounced with FO-NONP in comparison with FO. In conclusion, when applied to real datasets and evaluated by numerical predictive checks, the nonparametric estimation methods in NONMEM VI performed better than the corresponding parametric methods (FO or FOCE).
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| 3. |
- Baverel, Paul G., et al.
(författare)
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Two bootstrapping routines for obtaining imprecision estimates for nonparametric parameter distributions in nonlinear mixed effects models
- 2011
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 38:1, s. 63-82
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Tidskriftsartikel (refereegranskat)abstract
- When parameter estimates are used in predictions or decisions, it is important to consider the magnitude of imprecision associated with the estimation. Such imprecision estimates are, however, presently lacking for nonparametric algorithms intended for nonlinear mixed effects models. The objective of this study was to develop resampling-based methods for estimating imprecision in nonparametric distribution (NPD) estimates obtained in NONMEM. A one-compartment PK model was used to simulate datasets for which the random effect of clearance conformed to a (i) normal (ii) bimodal and (iii) heavy-tailed underlying distributional shapes. Re-estimation was conducted assuming normality under FOCE, and NPDs were estimated sequential to this step. Imprecision in the NPD was then estimated by means of two different resampling procedures. The first (full) method relies on bootstrap sampling from the raw data and a re-estimation of both the preceding parametric (FOCE) and the nonparametric step. The second (simplified) method relies on bootstrap sampling of individual nonparametric probability distributions. Nonparametric 95% confidence intervals (95% CIs) were obtained and mean errors (MEs) of the 95% CI width were computed. Standard errors (SEs) of nonparametric population estimates were obtained using the simplified method and evaluated through 100 stochastic simulations followed by estimations (SSEs). Both methods were successfully implemented to provide imprecision estimates for NPDs. The imprecision estimates adequately reflected the reference imprecision in all distributional cases and regardless of the numbers of individuals in the original data. Relative MEs of the 95% CI width of CL marginal density when original data contained 200 individuals were equal to: (i) -22 and -12%, (ii) -22 and -9%, (iii) -13 and -5% for the full and simplified (n = 100), respectively. SEs derived from the simplified method were consistent with the ones obtained from 100 SSEs. In conclusion, two novel bootstrapping methods intended for nonparametric estimation methods are proposed. In addition of providing information about the precision of nonparametric parameter estimates, they can serve as diagnostic tools for the detection of misspecified parameter distributions.
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| 4. |
- Delattre, Maud, et al.
(författare)
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Analysis of exposure-response of CI-945 in patients with epilepsy : application of novel mixed hidden Markov modeling methodology
- 2012
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 39:3, s. 263-271
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Tidskriftsartikel (refereegranskat)abstract
- We propose to describe exposure-response relationship of an antiepileptic agent, using mixed hidden Markov modeling methodology, to reveal additional insights in the mode of the drug action which the novel approach offers. Daily seizure frequency data from six clinical studies including patients who received gabapentin were available for the analysis. In the model, seizure frequencies are governed by underlying unobserved disease activity states. Individual neighbouring states are dependent, like in reality and they exhibit their own dynamics with patients transitioning between low and high disease states, according to a set of transition probabilities. Our methodology enables estimation of unobserved disease dynamics and daily seizure frequencies in all disease states. Additional modes of drug action are achievable: gabapentin may influence both daily seizure frequencies and disease state dynamics. Gabapentin significantly reduced seizure frequencies in both disease activity states; however it did not significatively affect disease dynamics. Mixed hidden Markov modeling is able to mimic dynamics of seizure frequencies very well. It offers novel insights into understanding disease dynamics in epilepsy and gabapentin mode of action.
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| 5. |
- Guiastrennec, Benjamin, et al.
(författare)
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Suboptimal Antituberculosis Drug Concentrations and Outcomes in Small and HIV-Coinfected Children in India: Recommendations for Dose Modifications
- 2018
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Ingår i: Clinical Pharmacology and Therapeutics. - : Wiley. - 0009-9236 .- 1532-6535. ; 104:4, s. 733-741
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Tidskriftsartikel (refereegranskat)abstract
- This work aimed to evaluate the once‐daily antituberculosis treatment as recommended by the new Indian pediatric guidelines. Isoniazid, rifampin, and pyrazinamide concentration–time profiles and treatment outcome were obtained from 161 Indian children with drug‐sensitive tuberculosis undergoing thrice‐weekly dosing as per previous Indian pediatric guidelines. The exposure–response relationships were established using a population pharmacokinetic‐pharmacodynamic approach. Rifampin exposure was identified as the unique predictor of treatment outcome. Consequently, children with low body weight (4–7 kg) and/or HIV infection, who displayed the lowest rifampin exposure, were associated with the highest probability of unfavorable treatment (therapy failure, death) outcome (Punfavorable). Model‐based simulation of optimized (Punfavorable ≤ 5%) rifampin once‐daily doses were suggested per treatment weight band and HIV coinfection status (33% and 190% dose increase, respectively, from the new Indian guidelines). The established dose‐exposure–response relationship could be pivotal in the development of future pediatric tuberculosis treatment guidelines.
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| 6. |
- Kang, Dongwoo, et al.
(författare)
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Standard Error of Empirical Bayes Estimate in NONMEM (R) VI
- 2012
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Ingår i: Korean Journal of Physiology & Pharmacology. - : The Korean Physiological Society and The Korean Society of Pharmacology. - 1226-4512 .- 2093-3827. ; 16:2, s. 97-106
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Tidskriftsartikel (refereegranskat)abstract
- The pharmacokinetics/pharmacodynamics analysis software NONMEM (R) output provides model parameter estimates and associated standard errors. However, the standard error of empirical Bayes estimates of inter-subject variability is not available. A simple and direct method for estimating standard error of the empirical Bayes estimates of inter-subject variability using the NONMEM (R) VI internal matrix POSTV is developed and applied to several pharmacokinetic models using intensively or sparsely sampled data for demonstration and to evaluate performance. The computed standard error is in general similar to the results from other post-processing methods and the degree of difference, if any, depends on the employed estimation options.
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| 7. |
- Karlsson, Kristin C., et al.
(författare)
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Modeling subpopulations with the $MIXTURE subroutine in NONMEM : finding the individual probability of belonging to a subpopulation for the use in model analysis and improved decision making
- 2009
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Ingår i: AAPS Journal. - : Springer. - 1550-7416. ; 11:1, s. 148-154
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Tidskriftsartikel (refereegranskat)abstract
- In nonlinear mixed effects modeling using NONMEM, mixture models can be used for multimodal distributions of parameters. The fraction of individuals belonging to each of the subpopulations can be estimated, and the most probable subpopulation for each patient is output (MIXEST(k)). The objective function value (OFV) that is minimized is the sum of the OFVs for each patient (OFV(i)), which in turn is the sum across the k subpopulations (OFV(i,k)). The OFV(i,k) values can be used together with the total probability in the population of belonging to subpopulation k to calculate the individual probability of belonging to the subpopulation (IP(k)). Our objective was to explore the information gained by using IP(k) instead of or in addition to MIXEST(k) in the analysis of mixture models. Two real data sets described previously by mixture models as well as simulations were used to explore the use of IP(k) and the precision of individual parameter values based on IP(k) and MIXEST(k). For both real data-based mixture models, a substantial fraction (11% and 26%) of the patients had IP(k) values not close to 0 or 1 (IP(k) between 0.25 and 0.75). Simulations of eight different scenarios showed that individual parameter estimates based on MIXEST were less precise than those based on IP(k), as the root mean squared error was reduced for IP(k) in all scenarios. A probability estimate such as IP(k) provides more detailed information about each individual than the discrete MIXEST(k). Individual parameter estimates based on IP(k) should be preferable whenever individual parameter estimates are to be used as study output or for simulations.
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| 8. |
- Karlsson, Mats, et al.
(författare)
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Diagnosing Model Diagnostics
- 2007
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Ingår i: Clinical Pharmacology and Therapeutics. - : Springer Science and Business Media LLC. - 0009-9236 .- 1532-6535. ; 82, s. 17-20
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Tidskriftsartikel (refereegranskat)abstract
- Conclusions from clinical trial results that are derived from model-based analyses rely on the model adequately describing the underlying system. The traditionally used diagnostics intended to provide information about model adequacy have seldom discussed shortcomings. Without an understanding of the properties of these diagnostics, development and use of new diagnostics, and additional information pertaining to the diagnostics, there is risk that adequate models will be rejected and inadequate models accepted. Thus, a diagnosis of available diagnostics is desirable.
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| 9. |
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| 10. |
- Lopez-Varela, Elisa, et al.
(författare)
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Drug concentration at the site of disease in children with pulmonary tuberculosis
- 2022
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Ingår i: Journal of Antimicrobial Chemotherapy. - : Oxford University Press. - 0305-7453 .- 1460-2091. ; 77:6, s. 1710-1719
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Tidskriftsartikel (refereegranskat)abstract
- Background Current TB treatment for children is not optimized to provide adequate drug levels in TB lesions. Dose optimization of first-line antituberculosis drugs to increase exposure at the site of disease could facilitate more optimal treatment and future treatment-shortening strategies across the disease spectrum in children with pulmonary TB. Objectives To determine the concentrations of first-line antituberculosis drugs at the site of disease in children with intrathoracic TB. Methods We quantified drug concentrations in tissue samples from 13 children, median age 8.6 months, with complicated forms of pulmonary TB requiring bronchoscopy or transthoracic surgical lymph node decompression in a tertiary hospital in Cape Town, South Africa. Pharmacokinetic models were used to describe drug penetration characteristics and to simulate concentration profiles for bronchoalveolar lavage, homogenized lymph nodes, and cellular and necrotic lymph node lesions. Results Isoniazid, rifampicin and pyrazinamide showed lower penetration in most lymph node areas compared with plasma, while ethambutol accumulated in tissue. None of the drugs studied was able to reach target concentration in necrotic lesions. Conclusions Despite similar penetration characteristics compared with adults, low plasma exposures in children led to low site of disease exposures for all drugs except for isoniazid.
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