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Publications (10 of 24) Show all publications
Ibrahim, M. M. A., Ueckert, S., Freiberga, S., Kjellsson, M. C. & Karlsson, M. O. (2019). Model-Based Conditional Weighted Residuals Analysis for Structural Model Assessment. AAPS Journal, 21(3), Article ID UNSP 34.
Open this publication in new window or tab >>Model-Based Conditional Weighted Residuals Analysis for Structural Model Assessment
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2019 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 21, no 3, article id UNSP 34Article in journal (Refereed) Published
Abstract [en]

Nonlinear mixed effects models are widely used to describe longitudinal data to improve the efficiency of drug development process or increase the understanding of the studied disease. In such settings, the appropriateness of the modeling assumptions is critical in order to draw correct conclusions and must be carefully assessed for any substantial violations. Here, we propose a new method for structure model assessment, based on assessment of bias in conditional weighted residuals (CWRES). We illustrate this method by assessing prediction bias in two integrated models for glucose homeostasis, the integrated glucose-insulin (IGI) model, and the integrated minimal model (IMM). One dataset was simulated from each model then analyzed with the two models. CWRES outputted from each model fitting were modeled to capture systematic trends in CWRES as well as the magnitude of structural model misspecifications in terms of difference in objective function values (ΔOFVBias). The estimates of CWRES bias were used to calculate the corresponding bias in conditional predictions by the inversion of first-order conditional estimation method’s covariance equation. Time, glucose, and insulin concentration predictions were the investigated independent variables. The new method identified correctly the bias in glucose sub-model of the integrated minimal model (IMM), when this bias occurred, and calculated the absolute and proportional magnitude of the resulting bias. CWRES bias versus the independent variables agreed well with the true trends of misspecification. This method is fast easily automated diagnostic tool for model development/evaluation process, and it is already implemented as part of the Perl-speaks-NONMEM software.

Keywords
conditional weighted residuals, diagnostics, model evaluation, nonlinear mixed effects models, prediction bias, structural model
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-367054 (URN)10.1208/s12248-019-0305-2 (DOI)000460184300001 ()30815754 (PubMedID)
Available from: 2018-11-27 Created: 2018-11-27 Last updated: 2019-03-25Bibliographically approved
Buatoisi, S., Ueckert, S., Frey, N., Retout, S. & Mentre, F. (2018). A pharmacometric extension of MCP-MOD in dose finding studies. Journal of Pharmacokinetics and Pharmacodynamics, 45(Suppl. 1), S106-S106
Open this publication in new window or tab >>A pharmacometric extension of MCP-MOD in dose finding studies
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2018 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 45, no Suppl. 1, p. S106-S106Article in journal, Meeting abstract (Other academic) Published
Place, publisher, year, edition, pages
SPRINGER/PLENUM PUBLISHERS, 2018
National Category
Medicinal Chemistry
Identifiers
urn:nbn:se:uu:diva-365108 (URN)000445374700234 ()
Available from: 2018-11-16 Created: 2018-11-16 Last updated: 2018-11-16Bibliographically approved
Buatois, S., Ueckert, S., Frey, N., Retout, S. & Mentre, F. (2018). Comparison of Model Averaging and Model Selection in Dose Finding Trials Analyzed by Nonlinear Mixed Effect Models. AAPS Journal, 20(3), Article ID UNSP 56.
Open this publication in new window or tab >>Comparison of Model Averaging and Model Selection in Dose Finding Trials Analyzed by Nonlinear Mixed Effect Models
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2018 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 20, no 3, article id UNSP 56Article in journal (Refereed) Published
Abstract [en]

In drug development, pharmacometric approaches consist in identifying via a model selection (MS) process the model structure that best describes the data. However, making predictions using a selected model ignores model structure uncertainty, which could impair predictive performance. To overcome this drawback, model averaging (MA) takes into account the uncertainty across a set of candidate models by weighting them as a function of an information criterion. Our primary objective was to use clinical trial simulations (CTSs) to compare model selection (MS) with model averaging (MA) in dose finding clinical trials, based on the AIC information criterion. A secondary aim of this analysis was to challenge the use of AIC by comparing MA and MS using five different information criteria. CTSs were based on a nonlinear mixed effect model characterizing the time course of visual acuity in wet age-related macular degeneration patients. Predictive performances of the modeling approaches were evaluated using three performance criteria focused on the main objectives of a phase II clinical trial. In this framework, MA adequately described the data and showed better predictive performance than MS, increasing the likelihood of accurately characterizing the dose-response relationship and defining the minimum effective dose. Moreover, regardless of the modeling approach, AIC was associated with the best predictive performances.

Keywords
dose-response relationship, model averaging, model selection, nonlinear mixed effect models
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-356080 (URN)10.1208/s12248-018-0205-x (DOI)000431135700006 ()29600418 (PubMedID)
Available from: 2018-07-13 Created: 2018-07-13 Last updated: 2018-07-13Bibliographically approved
Ueckert, S. (2018). Modeling Composite Assessment Data Using Item Response Theory. CPT: Pharmacometrics & Systems Pharmacology, 7(4), 205-218
Open this publication in new window or tab >>Modeling Composite Assessment Data Using Item Response Theory
2018 (English)In: CPT: Pharmacometrics & Systems Pharmacology, ISSN 2163-8306, Vol. 7, no 4, p. 205-218Article in journal (Refereed) Published
Abstract [en]

Composite assessments aim to combine different aspects of a disease in a single score and are utilized in a variety of therapeutic areas. The data arising from these evaluations are inherently discrete with distinct statistical properties. This tutorial presents the framework of the item response theory (IRT) for the analysis of this data type in a pharmacometric context. The article considers both conceptual (terms and assumptions) and practical questions (modeling software, data requirements, and model building).

National Category
Pharmacology and Toxicology
Identifiers
urn:nbn:se:uu:diva-354244 (URN)10.1002/psp4.12280 (DOI)000430807500001 ()29493119 (PubMedID)
Available from: 2018-06-19 Created: 2018-06-19 Last updated: 2018-06-19Bibliographically approved
Novakovic, A. M., Krekels, E. H. .., Munafo, A., Ueckert, S. & Karlsson, M. O. (2017). Application of Item Response Theory to Modeling of Expanded Disability Status Scale in Multiple Sclerosis. AAPS Journal, 19(1), 172-179
Open this publication in new window or tab >>Application of Item Response Theory to Modeling of Expanded Disability Status Scale in Multiple Sclerosis
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2017 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 19, no 1, p. 172-179Article in journal (Other academic) Published
Abstract [en]

In this study, we report the development of the first IRT model within a NLME (Non Linear Mixed Effect) framework to characterize the disease progression in MS (as measured by EDSS). Data were collected from a 96-week Phase III clinical study, involving 104206 item-level observations from 1319 patients with relapsing-remitting MS, treated with placebo or cladribine. Observed scores for each EDSS item were modelled describing the probability of a given score as a function of patients’ (unobserved) disability using a logistic model. Longitudinal data from placebo arms were used to describe the disease progression over time and the model was then extended to cladribine arms in order to characterize the drug effect. Sensitivity with respect to patient disability was calculated as Fisher information for each EDSS item, which were ranked according to the amount of information they contained. IRT model was able to describe baseline and longitudinal EDSS data on item and total level. Final model suggested that cladribine treatment significantly slows disease-progression rate, with a 20% decrease in disease-progression rate compared to placebo, irrespective of exposure, and effects an additional exposure-dependent reduction in disability progression. Four out of 8 items contained 80% of information for the given range of disabilities. This study has illustrated that IRT modelling is specifically suitable for accurate quantification of disease status and description and prediction of disease progression in Phase 3 studies on RRMS, by integrating EDSS item-level data in a meaningful manner.

Keywords
multiple sclerosis, disease progression model, Expanded Disability Status Scale, Item Response Theory, cladribine tablets
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-315622 (URN)10.1208/s12248-016-9977-z (DOI)000392210900017 ()27634384 (PubMedID)
Funder
EU, European Research Council
Note

CLINICAL PHARMACOKINETICS, Volume: 58, Issue: 3, Pages: 401-401, DOI: 10.1007/s40262-018-0706-x

Available from: 2017-02-16 Created: 2017-02-16 Last updated: 2019-03-07Bibliographically approved
van Dijkman, S., Ueckert, S., Plan, E. L. & Karlsson, M. O. (2017). Differentiation and prognosis of healthy subjects, swedds and parkinson's patients using a multi-dimensional item response theory model. Paper presented at 23rd World Congress of Neurology (WCN), SEP 16-21, 2017, Kyoto, JAPAN. Journal of the Neurological Sciences, 381(Supplement), 97-98
Open this publication in new window or tab >>Differentiation and prognosis of healthy subjects, swedds and parkinson's patients using a multi-dimensional item response theory model
2017 (English)In: Journal of the Neurological Sciences, ISSN 0022-510X, E-ISSN 1878-5883, Vol. 381, no Supplement, p. 97-98Article in journal, Meeting abstract (Other academic) Published
National Category
Neurology
Identifiers
urn:nbn:se:uu:diva-356247 (URN)10.1016/j.jns.2017.08.317 (DOI)000427450300291 ()
Conference
23rd World Congress of Neurology (WCN), SEP 16-21, 2017, Kyoto, JAPAN
Note

Meeting Abstract: 291

Available from: 2018-07-27 Created: 2018-07-27 Last updated: 2018-07-27Bibliographically approved
Buatois, S., Retout, S., Frey, N. & Ueckert, S. (2017). Item Response Theory as an Efficient Tool to Describe a Heterogeneous Clinical Rating Scale in De Novo Idiopathic Parkinson's Disease Patients. Pharmaceutical research, 34(10), 2109-2118
Open this publication in new window or tab >>Item Response Theory as an Efficient Tool to Describe a Heterogeneous Clinical Rating Scale in De Novo Idiopathic Parkinson's Disease Patients
2017 (English)In: Pharmaceutical research, ISSN 0724-8741, E-ISSN 1573-904X, Vol. 34, no 10, p. 2109-2118Article in journal (Refereed) Published
Abstract [en]

This manuscript aims to precisely describe the natural disease progression of Parkinson's disease (PD) patients and evaluate approaches to increase the drug effect detection power. An item response theory (IRT) longitudinal model was built to describe the natural disease progression of 423 de novo PD patients followed during 48 months while taking into account the heterogeneous nature of the MDS-UPDRS. Clinical trial simulations were then used to compare drug effect detection power from IRT and sum of item scores based analysis under different analysis endpoints and drug effects. The IRT longitudinal model accurately describes the evolution of patients with and without PD medications while estimating different progression rates for the subscales. When comparing analysis methods, the IRT-based one consistently provided the highest power. IRT is a powerful tool which enables to capture the heterogeneous nature of the MDS-UPDRS.

Place, publisher, year, edition, pages
SPRINGER/PLENUM PUBLISHERS, 2017
Keywords
drug effect, item response theory, MDS-UPDRS, Parkinson's disease, pharmacometrics
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-334753 (URN)10.1007/s11095-017-2216-1 (DOI)000409050300011 ()
Available from: 2017-11-27 Created: 2017-11-27 Last updated: 2018-01-13Bibliographically approved
Buatois, S., Ueckert, S., Frey, N., Retout, S. & Mentre, F. (2017). Modelling approaches in dose finding clinical trial: Simulation-based study comparing predictive performances of model averaging and model selection.. Journal of Pharmacokinetics and Pharmacodynamics, 44, S16-S17
Open this publication in new window or tab >>Modelling approaches in dose finding clinical trial: Simulation-based study comparing predictive performances of model averaging and model selection.
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2017 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 44, p. S16-S17Article in journal, Meeting abstract (Other academic) Published
Place, publisher, year, edition, pages
Springer, 2017
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-350692 (URN)000425764400016 ()
Available from: 2018-05-18 Created: 2018-05-18 Last updated: 2018-05-18Bibliographically approved
Ueckert, S., Karlsson, M. O. & Hooker, A. C. (2016). Accelerating Monte Carlo power studies through parametric power estimation. Journal of Pharmacokinetics and Pharmacodynamics, 43(2), 223-234
Open this publication in new window or tab >>Accelerating Monte Carlo power studies through parametric power estimation
2016 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 43, no 2, p. 223-234Article in journal (Refereed) Published
Abstract [en]

Estimating the power for a non-linear mixed-effects model-based analysis is challenging due to the lack of a closed form analytic expression. Often, computationally intensive Monte Carlo studies need to be employed to evaluate the power of a planned experiment. This is especially time consuming if full power versus sample size curves are to be obtained. A novel parametric power estimation (PPE) algorithm utilizing the theoretical distribution of the alternative hypothesis is presented in this work. The PPE algorithm estimates the unknown non-centrality parameter in the theoretical distribution from a limited number of Monte Carlo simulation and estimations. The estimated parameter linearly scales with study size allowing a quick generation of the full power versus study size curve. A comparison of the PPE with the classical, purely Monte Carlo-based power estimation (MCPE) algorithm for five diverse pharmacometric models showed an excellent agreement between both algorithms, with a low bias of less than 1.2 % and higher precision for the PPE. The power extrapolated from a specific study size was in a very good agreement with power curves obtained with the MCPE algorithm. PPE represents a promising approach to accelerate the power calculation for non-linear mixed effect models.

Keywords
Hypothesis test; Monte Carlo method; NONMEM; Non-linear mixed effect models; Power
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:uu:diva-287520 (URN)10.1007/s10928-016-9468-y (DOI)000374704100008 ()26934878 (PubMedID)
Funder
EU, FP7, Seventh Framework Programme, 602552
Available from: 2016-04-25 Created: 2016-04-25 Last updated: 2017-11-30Bibliographically approved
Novakovic, A., Krekels, E. H., Savic, R., Munafo, A., Ueckert, S. & Karlsson, M. O. (2015). Covariate analysis using item response theory modelling of expanded disability status scale (EDSS): a case study of cladribine tablets. Paper presented at 31st Congress of the European-Committee-for-Treatment-and-Research-in-Multiple-Sclerosis (ECTRIMS), OCT 07-10, 2015, Barcelona, SPAIN. Multiple Sclerosis, 21, 704-705
Open this publication in new window or tab >>Covariate analysis using item response theory modelling of expanded disability status scale (EDSS): a case study of cladribine tablets
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2015 (English)In: Multiple Sclerosis, ISSN 1352-4585, E-ISSN 1477-0970, Vol. 21, p. 704-705Article in journal, Meeting abstract (Other academic) Published
National Category
Neurology
Identifiers
urn:nbn:se:uu:diva-276954 (URN)000365729402212 ()
Conference
31st Congress of the European-Committee-for-Treatment-and-Research-in-Multiple-Sclerosis (ECTRIMS), OCT 07-10, 2015, Barcelona, SPAIN
Available from: 2016-02-23 Created: 2016-02-16 Last updated: 2017-11-30Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-3712-0255

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