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  • 1.
    Bell, Joanne
    et al.
    Pfizer Inc.
    ONeill, Brian
    Pfizer Inc.
    Brodney, Michael
    Pfizer Inc.
    Hajos-Korcsok, Eva
    Pfizer Inc.
    Lu, Yasong
    Pfizer Inc.
    Riddell, David
    Pfizer Inc.
    Ito, Kaori
    Pfizer Inc.
    Ueckert, Sebastian
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Nicholas, Timothy
    Pfizer Inc.
    A novel BACE inhibitor (PF-05297909):: A two-part adaptive design to evaluate safety, pharmacokinetics and pharmacodynamics for modifying beta-amyloid in a first-in-human study2013Ingår i: Alzheimer's & Dementia, ISSN 1552-5260, E-ISSN 1552-5279, Vol. 9, nr 4, s. P287-Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Background

    The accumulation of amyloid beta (Aβ) peptides is believed to be a central contributor to the neurodegeneration seen in the Alzheimer's disease (AD) brain. Given the central role of Aβ42 in AD pathogenesis, a therapeutic strategy to lower central Aβ42 (and Aβ40) levels via inhibition of BACE was adopted in a first in human trial in a 2-part adaptive design.

    Methods

    Part 1 evaluated PF-05297909 plasma PK and the PK/PD relationship for the reduction of plasma Aβ40, Aβ42 and AβX levels; Part 2 evaluated the exposure-response relationship between PF-05297909 and CSF levels of Aβ40, Aβ42 and AβX. Sufficient safety and tolerability, plasma exposure and reduction in plasma Aβ were necessary to initiate Part 2. Part 1 was a sequential parallel group dose escalation (25, 100, 250 and 325 mg) with n=8 (6:2, active:placebo) healthy volunteers (HV) in each cohort. Part 2 consisted of 3 cohorts of n=8 (6:2, active:placebo) HV. Doses selected for Part 2 started with the highest safe dose in Part 1 and then adapted for subsequent cohorts. The PK/PD relationship between PF-05297909 and Aβ42 was determined using a non-linear mixed effects (NLME) analysis. The doses for Part 2 - cohort 2 and 3 were to be chosen to improve the relative standard error in the estimate of the BACE IC50 as quantified by evaluating the determinant of the Fisher information matrix for the NLME model.

    Results

    PF-05297909 was well-tolerated. Reduction in plasma Aβ (Aβ40 and Aβ42) was exposure related with an apparent maximum at the 250 mg dose with a greater duration of activity at the 325 mg dose of PF-05297909. A 325 mg dose was selected for Part 2 - cohorts 1 and 2 without further cohorts being run, as stopping criteria for futility were met following analysis of cohort 2. A PK/PD relationship in CSF was not observed.

    Conclusions

    The adaptive designed PF-05297909 FIH study allowed efficient testing of safety and of the PK/PD relationship between PF-05297909 exposure and Aβ (Aβ40 and Aβ42). PF-05297909 was safe and well tolerated in HV at exposures tested. A robust effect on plasma Aβ did not translate to CSF pharmacodynamic effects.

  • 2.
    Buatois, S.
    et al.
    F Hoffmann La Roche Ltd, Roche Innovat Ctr Basel, Clin Pharmacol, Roche Pharma Res & Early Dev, Grenzacherstr 124, CH-4070 Basel, Switzerland.;INSERM, UMR 1137, IAME, F-75018 Paris, France..
    Ueckert, Sebastian
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Frey, N.
    F Hoffmann La Roche Ltd, Roche Innovat Ctr Basel, Clin Pharmacol, Roche Pharma Res & Early Dev, Grenzacherstr 124, CH-4070 Basel, Switzerland..
    Retout, S.
    F Hoffmann La Roche Ltd, Roche Innovat Ctr Basel, Clin Pharmacol, Roche Pharma Res & Early Dev, Grenzacherstr 124, CH-4070 Basel, Switzerland..
    Mentre, F.
    INSERM, UMR 1137, IAME, F-75018 Paris, France..
    Modelling approaches in dose finding clinical trial: Simulation-based study comparing predictive performances of model averaging and model selection.2017Ingår i: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 44, s. S16-S17Artikel i tidskrift (Övrigt vetenskapligt)
  • 3.
    Buatois, Simon
    et al.
    F Hoffmann La Roche Ltd, Roche Pharma Res & Early Dev, Clin Pharmacol Roche Innovat Ctr Basel, Grenzacherstr 124, CH-4070 Basel, Switzerland.;Univ Paris Diderot, IAME, UMR 1137, INSERM,Sorbonne Paris Cite, Paris, France.;Roche SAS, Inst Roche, 30,Cours Ile Seguin, F-92650 Boulogne, France..
    Retout, Sylvie
    F Hoffmann La Roche Ltd, Roche Pharma Res & Early Dev, Clin Pharmacol Roche Innovat Ctr Basel, Grenzacherstr 124, CH-4070 Basel, Switzerland.;Roche SAS, Inst Roche, 30,Cours Ile Seguin, F-92650 Boulogne, France..
    Frey, Nicolas
    F Hoffmann La Roche Ltd, Roche Pharma Res & Early Dev, Clin Pharmacol Roche Innovat Ctr Basel, Grenzacherstr 124, CH-4070 Basel, Switzerland..
    Ueckert, Sebastian
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Item Response Theory as an Efficient Tool to Describe a Heterogeneous Clinical Rating Scale in De Novo Idiopathic Parkinson's Disease Patients2017Ingår i: Pharmaceutical research, ISSN 0724-8741, E-ISSN 1573-904X, Vol. 34, nr 10, s. 2109-2118Artikel i tidskrift (Refereegranskat)
    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.

  • 4.
    Buatois, Simon
    et al.
    F Hoffmann La Rache Ltd, Roche Innovat Ctr Basel, Roche Pharma Res & Early Dev, Pharmaceut Sci, Basel, Switzerland; Inst Roche, Boulogne, France; Univ Paris Diderot, Sorbonne Paris Cite, INSERM, UMR 1137, IAME, Paris, France.
    Ueckert, Sebastian
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Frey, Nicolas
    F Hoffmann La Rache Ltd, Roche Innovat Ctr Basel, Roche Pharma Res & Early Dev, Pharmaceut Sci, Basel, Switzerland.
    Retout, Sylvie
    F Hoffmann La Rache Ltd, Roche Innovat Ctr Basel, Roche Pharma Res & Early Dev, Pharmaceut Sci, Basel, Switzerland; Inst Roche, Boulogne, France.
    Mentre, France
    Univ Paris Diderot, Sorbonne Paris Cite, INSERM, UMR 1137, IAME, Paris, France.
    Comparison of Model Averaging and Model Selection in Dose Finding Trials Analyzed by Nonlinear Mixed Effect Models2018Ingår i: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 20, nr 3, artikel-id UNSP 56Artikel i tidskrift (Refereegranskat)
    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.

  • 5.
    Buatoisi, Simon
    et al.
    Univ Paris Diderot, IAME, UMR 1137, INSERM,Sorbonne Paris Cite, Paris, France.;F Hoffmann La Roche Ltd, Roche Pharma Res & Early Dev, Pharmaceut Sci, Roche Innovat Ctr Basel, Grenzacherstr 124, CH-4070 Basel, Switzerland.;INST ROCHE, 30 Cours Ile Seguin, F-92650 Boulogne, France..
    Ueckert, Sebastian
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Frey, Nicolas
    F Hoffmann La Roche Ltd, Roche Pharma Res & Early Dev, Pharmaceut Sci, Roche Innovat Ctr Basel, Grenzacherstr 124, CH-4070 Basel, Switzerland..
    Retout, Sylvie
    F Hoffmann La Roche Ltd, Roche Pharma Res & Early Dev, Pharmaceut Sci, Roche Innovat Ctr Basel, Grenzacherstr 124, CH-4070 Basel, Switzerland.;INST ROCHE, 30 Cours Ile Seguin, F-92650 Boulogne, France..
    Mentre, France
    Univ Paris Diderot, IAME, UMR 1137, INSERM,Sorbonne Paris Cite, Paris, France..
    A pharmacometric extension of MCP-MOD in dose finding studies2018Ingår i: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 45, nr Suppl. 1, s. S106-S106Artikel i tidskrift (Övrigt vetenskapligt)
  • 6.
    Ibrahim, Moustafa M. A.
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Ueckert, Sebastian
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Freiberga, Svetlana
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Kjellsson, Maria C.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Karlsson, Mats O.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Model-Based Conditional Weighted Residuals Analysis for Structural Model Assessment2019Ingår i: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 21, nr 3, artikel-id UNSP 34Artikel i tidskrift (Refereegranskat)
    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.

  • 7.
    Johansson, Åsa M.
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Ueckert, Sebastian
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Plan, Elodie L.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Hooker, Andrew C.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Karlsson, Mats O.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Evaluation of Bias, Precision, Robustness and Runtime for Estimation Methods in NONMEM 72014Ingår i: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 41, nr 3, s. 223-238Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    NONMEM is the most widely used software for population pharmacokinetic (PK)-pharmacodynamic (PD) analyses. The latest version, NONMEM 7 (NM7), includes several sampling-based estimation algorithms in addition to the classical algorithms. In this study, performance of the estimation algorithms available in NM7 was investigated with respect to bias, precision, robustness and runtime for a diverse set of PD models. Simulations of 500 data sets from each PD model were reanalyzed with the available estimation algorithms to investigate bias and precision. Simulations of 100 data sets were used to investigate robustness by comparing final estimates obtained after estimations starting from the true parameter values and initial estimates randomly generated using the CHAIN feature in NM7. Average estimation time for each algorithm and each model was calculated from the runtimes reported by NM7.

    The algorithm giving the lowest bias and highest precision across models was importance sampling (IMP), closely followed by FOCE/LAPLACE and stochastic approximation expectation-maximization (SAEM). The algorithms relative robustness differed between models, but FOCE/LAPLACE was the most robust algorithm across models, followed by SAEM and IMP. FOCE/LAPLACE was also the algorithm with the shortest runtime for all models, followed by iterative two-stage (ITS). The Bayesian Markov Chain Monte Carlo method, used in this study for point estimation, performed worst in all tested metrics.

  • 8.
    Novakovic, A.
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Krekels, E. H. J.
    Leiden Univ, Leiden Acad Ctr Drug Res, Div Pharmacol, Leiden, Netherlands..
    Savic, R.
    Univ Calif San Francisco, Dept Bioengn & Therapeut Sci, San Francisco, CA 94143 USA..
    Munafo, A.
    Merck Serono, Merck Inst Pharmacometr, Lausanne, Switzerland..
    Ueckert, Sebastian
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Karlsson, Mats O.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Covariate analysis using item response theory modelling of expanded disability status scale (EDSS): a case study of cladribine tablets2015Ingår i: Multiple Sclerosis, ISSN 1352-4585, E-ISSN 1477-0970, Vol. 21, s. 704-705Artikel i tidskrift (Övrigt vetenskapligt)
  • 9.
    Novakovic, Ana M.
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Krekels, Elke H.J.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap. Division of Pharmacology, Leiden Academic Centre of Drug Research, Leiden University, Leiden, Netherlands.
    Munafo, Alain
    Merck Institute of Pharmacometrics, Lausanne, Switzerland.
    Ueckert, Sebastian
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Karlsson, Mats O
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Application of Item Response Theory to Modeling of Expanded Disability Status Scale in Multiple Sclerosis2017Ingår i: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 19, nr 1, s. 172-179Artikel i tidskrift (Övrigt vetenskapligt)
    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.

  • 10.
    Nyberg, Joakim
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Ueckert, Sebastian
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Strömberg, Eric
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Hennig, Stefanie
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Karlsson, Mats O.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Hooker, Andrew C.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool2012Ingår i: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 108, nr 2, s. 789-805Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Several developments have facilitated the practical application and increased the general use of optimal design for nonlinear mixed effects models. These developments include new methodology for utilizing advanced pharmacometric models, faster optimization algorithms and user friendly software tools. In this paper we present the extension of theoptimal design software PopED, which incorporates many of these recent advances into aneasily useable enhanced GUI. Furthermore, we present new solutions to problems related to the design of experiments such as: faster and more robust FIM calculations and optimizations, optimizing over cost/utility functions and diagnostic tools and plots to evaluate designperformance. Examples for; (i) Group size optimization and efficiency translation, (ii) Cost/constraint optimization, (iii) Optimizations with different FIM approximations and (iv) optimization with parallel computing demonstrate the new features in PopED and underline the potential use of this tool when designing experiments. 

  • 11.
    Ueckert, Sebastian
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Modeling Composite Assessment Data Using Item Response Theory2018Ingår i: CPT: Pharmacometrics & Systems Pharmacology, ISSN 2163-8306, Vol. 7, nr 4, s. 205-218Artikel i tidskrift (Refereegranskat)
    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).

  • 12.
    Ueckert, Sebastian
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Novel Pharmacometric Methods for Design and Analysis of Disease Progression Studies2014Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    With societies aging all around the world, the global burden of degenerative diseases is expected to increase exponentially. From the perspective drug development, degenerative diseases represent an especially challenging class. Clinical trials, in this context often termed disease progression studies, are long, costly, require many individuals, and have low success rates. Therefore, it is crucial to use informative study designs and to analyze efficiently the obtained trial data. The development of novel approaches intended towards facilitating both the design and the analysis of disease progression studies was the aim of this thesis.

    This aim was pursued in three stages (i) the characterization and extension of pharmacometric software, (ii) the development of new methodology around statistical power, and (iii) the demonstration of application benefits.

    The optimal design software PopED was extended to simplify the application of optimal design methodology when planning a disease progression study. The performance of non-linear mixed effect estimation algorithms for trial data analysis was evaluated in terms of bias, precision, robustness with respect to initial estimates, and runtime. A novel statistic allowing for explicit optimization of study design for statistical power was derived and found to perform superior to existing methods. Monte-Carlo power studies were accelerated through application of parametric power estimation, delivering full power versus sample size curves from a few hundred Monte-Carlo samples. Optimal design and an explicit optimization for statistical power were applied to the planning of a study in Alzheimer's disease, resulting in a 30% smaller study size when targeting 80% power. The analysis of ADAS-cog score data was improved through application of item response theory, yielding a more exact description of the assessment score, an increased statistical power and an enhanced insight in the assessment properties.

    In conclusion, this thesis presents novel pharmacometric methods that can help addressing the challenges of designing and planning disease progression studies.

    Delarbeten
    1. PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool
    Öppna denna publikation i ny flik eller fönster >>PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool
    Visa övriga...
    2012 (Engelska)Ingår i: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 108, nr 2, s. 789-805Artikel i tidskrift (Refereegranskat) Published
    Abstract [en]

    Several developments have facilitated the practical application and increased the general use of optimal design for nonlinear mixed effects models. These developments include new methodology for utilizing advanced pharmacometric models, faster optimization algorithms and user friendly software tools. In this paper we present the extension of theoptimal design software PopED, which incorporates many of these recent advances into aneasily useable enhanced GUI. Furthermore, we present new solutions to problems related to the design of experiments such as: faster and more robust FIM calculations and optimizations, optimizing over cost/utility functions and diagnostic tools and plots to evaluate designperformance. Examples for; (i) Group size optimization and efficiency translation, (ii) Cost/constraint optimization, (iii) Optimizations with different FIM approximations and (iv) optimization with parallel computing demonstrate the new features in PopED and underline the potential use of this tool when designing experiments. 

    Nationell ämneskategori
    Farmaceutiska vetenskaper
    Identifikatorer
    urn:nbn:se:uu:diva-160475 (URN)10.1016/j.cmpb.2012.05.005 (DOI)000310828200030 ()
    Tillgänglig från: 2011-10-24 Skapad: 2011-10-24 Senast uppdaterad: 2018-01-12
    2. Evaluation of Bias, Precision, Robustness and Runtime for Estimation Methods in NONMEM 7
    Öppna denna publikation i ny flik eller fönster >>Evaluation of Bias, Precision, Robustness and Runtime for Estimation Methods in NONMEM 7
    Visa övriga...
    2014 (Engelska)Ingår i: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 41, nr 3, s. 223-238Artikel i tidskrift (Refereegranskat) Published
    Abstract [en]

    NONMEM is the most widely used software for population pharmacokinetic (PK)-pharmacodynamic (PD) analyses. The latest version, NONMEM 7 (NM7), includes several sampling-based estimation algorithms in addition to the classical algorithms. In this study, performance of the estimation algorithms available in NM7 was investigated with respect to bias, precision, robustness and runtime for a diverse set of PD models. Simulations of 500 data sets from each PD model were reanalyzed with the available estimation algorithms to investigate bias and precision. Simulations of 100 data sets were used to investigate robustness by comparing final estimates obtained after estimations starting from the true parameter values and initial estimates randomly generated using the CHAIN feature in NM7. Average estimation time for each algorithm and each model was calculated from the runtimes reported by NM7.

    The algorithm giving the lowest bias and highest precision across models was importance sampling (IMP), closely followed by FOCE/LAPLACE and stochastic approximation expectation-maximization (SAEM). The algorithms relative robustness differed between models, but FOCE/LAPLACE was the most robust algorithm across models, followed by SAEM and IMP. FOCE/LAPLACE was also the algorithm with the shortest runtime for all models, followed by iterative two-stage (ITS). The Bayesian Markov Chain Monte Carlo method, used in this study for point estimation, performed worst in all tested metrics.

    Nyckelord
    NONMEM, estimation algorithms
    Nationell ämneskategori
    Farmaceutiska vetenskaper
    Forskningsämne
    Farmaceutisk vetenskap
    Identifikatorer
    urn:nbn:se:uu:diva-216136 (URN)10.1007/s10928-014-9359-z (DOI)000338496300003 ()24801864 (PubMedID)
    Tillgänglig från: 2014-01-19 Skapad: 2014-01-19 Senast uppdaterad: 2018-01-11Bibliografiskt granskad
    3. Optimizing disease progression study designs for drug effect discrimination
    Öppna denna publikation i ny flik eller fönster >>Optimizing disease progression study designs for drug effect discrimination
    Visa övriga...
    2013 (Engelska)Ingår i: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 40, nr 5, s. 587-596Artikel i tidskrift (Refereegranskat) Published
    Abstract [en]

    Investigate the possibility to directly optimize a clinical trial design for statistical power to detect a drug effect and compare to optimal designs that focus on parameter precision. An improved statistic derived from the general formulation of the Wald approximation was used to predict the statistical power for given trial designs of a disease progression study. The predicted value was compared, together with the classical Wald statistic, to a type I error-corrected model-based power determined via clinical trial simulations. In a second step, a study design for maximal power was determined by directly maximizing the new statistic. The resulting power-optimal designs and their corresponding performance based on empirical power calculations were compared to designs focusing on parameter precision. Comparisons of empirically determined power and the newly developed statistic, showed excellent agreement across all scenarios investigated. This was in contrast to the classical Wald statistic, which consistently over-predicted the reference power with deviations of up to 90 %. Designs maximized using the proposed metric differed from traditional optimal designs and showed equal or up to 20 % higher power in the subsequent clinical trial simulations. Furthermore, the proposed method was used to minimize the number of individuals required to achieve 80 % power through a simultaneous optimization of study size and study design. The targeted power of 80 % was confirmed in subsequent simulation study. A new statistic was developed, allowing for the explicit optimization of a clinical trial design with respect to statistical power.

    Nyckelord
    Optimal experimental design, Statistical power, Wald test, Disease progression studies
    Nationell ämneskategori
    Medicin och hälsovetenskap
    Identifikatorer
    urn:nbn:se:uu:diva-210213 (URN)10.1007/s10928-013-9331-3 (DOI)000325263800004 ()
    Tillgänglig från: 2013-11-04 Skapad: 2013-11-04 Senast uppdaterad: 2017-12-06
    4. Accelerating Monte-Carlo Power Studies through Parametric Power Estimation
    Öppna denna publikation i ny flik eller fönster >>Accelerating Monte-Carlo Power Studies through Parametric Power Estimation
    (Engelska)Manuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    Estimating the power of a future clinical study is a common problem in the drug development process. Within the framework of model based drug development this problem is solved through Monte-Carlo studies where numerous replicates of the trial are simulated and subsequently analysed. This process can be very time consuming due to the high number of replicates required to obtain a stable power estimate. Non-linear mixed effect models which are frequently used for the analysis of clinical trial data are especially problematic as they can have a run time of several hours.

    A novel parametric power estimation (PPE) algorithm utilizing the theoretical distribution of the alternative hypothesis is presented in this work and compared to classical Monte-Carlo studies. The PPE algorithm estimates the unknown non-centrality parameter in the theoretical distribution from a limited number of Monte-Carlo simulation and estimations. Furthermore, from the estimated parameter a complete power versus sample size curve can be obtained analytically without additional simulations. The PPE and classical Monte-Carlo algorithms were compared for 3 different drug development examples.

    For a single power calculation, given a specific sample size, the PPE algorithm provided accurate estimates for all investigated scenarios and required 2 times fewer samples than the pure Monte-Carlo method to achieve the same level of precision. Furthermore, from this single power calculation, the PPE method can derive an entire power curve (power versus sample size), drastically reducing run times for this computation. The power curves from the PPE algorithm were in excellent agreement with the curves obtained using classical Monte-Carlo techniques.

    Nationell ämneskategori
    Farmaceutiska vetenskaper
    Forskningsämne
    Farmaceutisk vetenskap
    Identifikatorer
    urn:nbn:se:uu:diva-216528 (URN)
    Tillgänglig från: 2014-01-22 Skapad: 2014-01-22 Senast uppdaterad: 2018-01-11
    5. Challenges and potential of optimal design in late phase clinical trials through application in Alzheimer’s disease
    Öppna denna publikation i ny flik eller fönster >>Challenges and potential of optimal design in late phase clinical trials through application in Alzheimer’s disease
    Visa övriga...
    (Engelska)Manuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    Optimal design is a methodology that can be a valuable tool for the planning of clinical studies. Current applications however, are largely limited to early phases of the drug development process. The increasing complexity in late phase trials is a major reason why optimal design is not applied at these stages. This work uses the example of Alzheimer's disease to investigate challenges and potential of applying optimal design in late phase clinical trials.

    Information from several sources was used to construct a disease progression model for Alzheimer's disease. The resulting model was used to optimize the study design of an Alzheimer's trial for three distinct metrics: maximal information, minimal number of samples and maximal power to detect a drug effect. Challenges encountered and addressed during the implementation included covariates, dropout and clinical constraints.

    Depending on the optimization criterion used, the optimal designs had 35% a higher efficiency, needed 33% fewer samples to obtain the same amount of information or required 70% fewer individuals to achieve 80% power compared to the reference design.

    Optimal design can improve the design and therefore reduce the costs of late phase trials. Several tools and techniques have been identified to address the main challenges connected to this application.

    Nationell ämneskategori
    Farmaceutiska vetenskaper Sannolikhetsteori och statistik
    Forskningsämne
    Farmaceutisk vetenskap
    Identifikatorer
    urn:nbn:se:uu:diva-215618 (URN)
    Tillgänglig från: 2014-01-15 Skapad: 2014-01-15 Senast uppdaterad: 2018-01-11
    6. Improved Utilization of ADAS-cog Assessment Data through Item Response Theory based Pharmacometric Modeling
    Öppna denna publikation i ny flik eller fönster >>Improved Utilization of ADAS-cog Assessment Data through Item Response Theory based Pharmacometric Modeling
    Visa övriga...
    2014 (Engelska)Ingår i: Pharmaceutical research, ISSN 0724-8741, E-ISSN 1573-904X, Vol. 31, nr 8, s. 2152-2165Artikel i tidskrift (Refereegranskat) Published
    Abstract [en]

    Purpose

    This work investigates improved utilization of ADAS-cog data (the primaryoutcome in Alzheimer's disease (AD) trials of mild and moderate AD) by combiningpharmacometric modeling and item response theory (IRT).

    Methods

    A baseline IRT model characterizing the ADAS-cog was built based on datafrom 2744 individuals. Pharmacometric methods were used to extend the baseline IRTmodel to describe longitudinal ADAS-cog scores from an 18-month clinical study with322 patients. Sensitivity of the ADAS-cog items in different patient populations as wellas the power to detect a drug effect in relation to total score base methods wereassessed with IRT based models.

    Results

    IRT analysis was able to describe both total and item level baseline ADAS-cogdata. Longitudinal data were also well described. Differences in the informationcontent of the item level components could be quantitatively characterized and rankedfor mild cognitively impairment and mild AD populations. Based on clinical trialsimulations with a theoretical drug effect, the IRT method demonstrated a significantlyhigher power to detect drug effect compared to the traditional method of analysis.

    Conclusion

    A combined framework of IRT and pharmacometric modeling permits amore effective and precise analysis than total score based methods and thereforeincreases the value of ADAS-cog data.

    Ort, förlag, år, upplaga, sidor
    Springer, 2014
    Nyckelord
    Alzheimer's disease, Item response theory, ADAS-cog, pharmacometrics, nonlinear mixed effect models
    Nationell ämneskategori
    Farmaceutiska vetenskaper
    Forskningsämne
    Farmaceutisk vetenskap
    Identifikatorer
    urn:nbn:se:uu:diva-216524 (URN)10.1007/s11095-014-1315-5 (DOI)000341712400026 ()
    Forskningsfinansiär
    EU, FP7, Sjunde ramprogrammet, 115156
    Tillgänglig från: 2014-01-22 Skapad: 2014-01-22 Senast uppdaterad: 2018-01-11Bibliografiskt granskad
  • 13.
    Ueckert, Sebastian
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Andrew, Marilee A.
    Amgen Inc.
    Karlsson, Mats
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Ito, Kaori
    Pfizer Inc.
    Corrigan, Brian
    Pfizer Inc.
    Hooker, Andrew C.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Challenges and potential of optimal design in late phase clinical trials through application in Alzheimer’s diseaseManuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    Optimal design is a methodology that can be a valuable tool for the planning of clinical studies. Current applications however, are largely limited to early phases of the drug development process. The increasing complexity in late phase trials is a major reason why optimal design is not applied at these stages. This work uses the example of Alzheimer's disease to investigate challenges and potential of applying optimal design in late phase clinical trials.

    Information from several sources was used to construct a disease progression model for Alzheimer's disease. The resulting model was used to optimize the study design of an Alzheimer's trial for three distinct metrics: maximal information, minimal number of samples and maximal power to detect a drug effect. Challenges encountered and addressed during the implementation included covariates, dropout and clinical constraints.

    Depending on the optimization criterion used, the optimal designs had 35% a higher efficiency, needed 33% fewer samples to obtain the same amount of information or required 70% fewer individuals to achieve 80% power compared to the reference design.

    Optimal design can improve the design and therefore reduce the costs of late phase trials. Several tools and techniques have been identified to address the main challenges connected to this application.

  • 14.
    Ueckert, Sebastian
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Hennig, Stefanie
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Nyberg, Joakim
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Karlsson, Mats O.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Hooker, Andrew C.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Optimizing disease progression study designs for drug effect discrimination2013Ingår i: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 40, nr 5, s. 587-596Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Investigate the possibility to directly optimize a clinical trial design for statistical power to detect a drug effect and compare to optimal designs that focus on parameter precision. An improved statistic derived from the general formulation of the Wald approximation was used to predict the statistical power for given trial designs of a disease progression study. The predicted value was compared, together with the classical Wald statistic, to a type I error-corrected model-based power determined via clinical trial simulations. In a second step, a study design for maximal power was determined by directly maximizing the new statistic. The resulting power-optimal designs and their corresponding performance based on empirical power calculations were compared to designs focusing on parameter precision. Comparisons of empirically determined power and the newly developed statistic, showed excellent agreement across all scenarios investigated. This was in contrast to the classical Wald statistic, which consistently over-predicted the reference power with deviations of up to 90 %. Designs maximized using the proposed metric differed from traditional optimal designs and showed equal or up to 20 % higher power in the subsequent clinical trial simulations. Furthermore, the proposed method was used to minimize the number of individuals required to achieve 80 % power through a simultaneous optimization of study size and study design. The targeted power of 80 % was confirmed in subsequent simulation study. A new statistic was developed, allowing for the explicit optimization of a clinical trial design with respect to statistical power.

  • 15.
    Ueckert, Sebastian
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Hooker, Andrew C.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Accelerating Monte-Carlo Power Studies through Parametric Power EstimationManuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    Estimating the power of a future clinical study is a common problem in the drug development process. Within the framework of model based drug development this problem is solved through Monte-Carlo studies where numerous replicates of the trial are simulated and subsequently analysed. This process can be very time consuming due to the high number of replicates required to obtain a stable power estimate. Non-linear mixed effect models which are frequently used for the analysis of clinical trial data are especially problematic as they can have a run time of several hours.

    A novel parametric power estimation (PPE) algorithm utilizing the theoretical distribution of the alternative hypothesis is presented in this work and compared to classical Monte-Carlo studies. The PPE algorithm estimates the unknown non-centrality parameter in the theoretical distribution from a limited number of Monte-Carlo simulation and estimations. Furthermore, from the estimated parameter a complete power versus sample size curve can be obtained analytically without additional simulations. The PPE and classical Monte-Carlo algorithms were compared for 3 different drug development examples.

    For a single power calculation, given a specific sample size, the PPE algorithm provided accurate estimates for all investigated scenarios and required 2 times fewer samples than the pure Monte-Carlo method to achieve the same level of precision. Furthermore, from this single power calculation, the PPE method can derive an entire power curve (power versus sample size), drastically reducing run times for this computation. The power curves from the PPE algorithm were in excellent agreement with the curves obtained using classical Monte-Carlo techniques.

  • 16.
    Ueckert, Sebastian
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Karlsson, Mats O.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Hooker, Andrew C
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Accelerating Monte Carlo power studies through parametric power estimation2016Ingår i: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 43, nr 2, s. 223-234Artikel i tidskrift (Refereegranskat)
    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.

  • 17.
    Ueckert, Sebastian
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Lockwood, Peter
    Pfizer Inc, Global Innovat Pharma Business, Clin Pharmacol, Groton, CT 06340 USA..
    Schwartz, Pam
    Pfizer Inc, Global Innovat Pharma Business, Clin Pharmacol, Groton, CT 06340 USA..
    Riley, Steve
    Pfizer Inc, Global Innovat Pharma Business, Clin Pharmacol, Groton, CT 06340 USA..
    Modeling the Neuropsychiatric Inventory (NPI) - Strengths and Weaknesses of a Multidimensional Item Response Theory Approach2015Ingår i: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 42, nr S1, s. S92-S92Artikel i tidskrift (Övrigt vetenskapligt)
  • 18.
    Ueckert, Sebastian
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Plan, Elodie L.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Ito, Kaori
    Karlsson, Mats O.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Corrigan, Brian
    Hooker, Andrew C.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Benefits of an Item Response Theory Based Analysis of ADAS-cog Assessments2013Ingår i: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 40, nr S1, s. S49-S49Artikel i tidskrift (Övrigt vetenskapligt)
  • 19.
    Ueckert, Sebastian
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Plan, Elodie L.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Ito, Kaori
    Pfizer Inc.
    Karlsson, Mats O.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Corrigan, Brian
    Pfizer Inc.
    Hooker, Andrew C.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Improved Utilization of ADAS-cog Assessment Data through Item Response Theory based Pharmacometric Modeling2014Ingår i: Pharmaceutical research, ISSN 0724-8741, E-ISSN 1573-904X, Vol. 31, nr 8, s. 2152-2165Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Purpose

    This work investigates improved utilization of ADAS-cog data (the primaryoutcome in Alzheimer's disease (AD) trials of mild and moderate AD) by combiningpharmacometric modeling and item response theory (IRT).

    Methods

    A baseline IRT model characterizing the ADAS-cog was built based on datafrom 2744 individuals. Pharmacometric methods were used to extend the baseline IRTmodel to describe longitudinal ADAS-cog scores from an 18-month clinical study with322 patients. Sensitivity of the ADAS-cog items in different patient populations as wellas the power to detect a drug effect in relation to total score base methods wereassessed with IRT based models.

    Results

    IRT analysis was able to describe both total and item level baseline ADAS-cogdata. Longitudinal data were also well described. Differences in the informationcontent of the item level components could be quantitatively characterized and rankedfor mild cognitively impairment and mild AD populations. Based on clinical trialsimulations with a theoretical drug effect, the IRT method demonstrated a significantlyhigher power to detect drug effect compared to the traditional method of analysis.

    Conclusion

    A combined framework of IRT and pharmacometric modeling permits amore effective and precise analysis than total score based methods and thereforeincreases the value of ADAS-cog data.

  • 20.
    Ueckert, Sebastian
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Plan, Elodie L.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Ito, Kaori
    Karlsson, Mats O.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Corrigan, Brian
    Hooker, Andrew C.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Pharmacometric Modeling of Clinical ADAS-cog Assessment Data using Item Response Theory2013Ingår i: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 40, nr S1, s. S92-S92Artikel i tidskrift (Övrigt vetenskapligt)
  • 21.
    Ueckert, Sebastian
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Plan, Elodie L.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Ito, Kaori
    Karlsson, Mats O.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Corrigan, Brian
    Hooker, Andrew C.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Predicting Baseline ADAS-cog Scores from Screening Information using Item Response Theory and Full Random Effect Covariate Modeling2013Ingår i: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 40, nr S1, s. S71-S72Artikel i tidskrift (Övrigt vetenskapligt)
  • 22.
    Ueckert, Sebastian
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap. INSERM, UMR 1137, IAME, Paris, France.;Univ Paris Diderot, Paris, France..
    Riviere, Marie-Karelle
    INSERM, UMR 1137, IAME, Paris, France.;Univ Paris Diderot, Paris, France..
    Mentre, France
    INSERM, UMR 1137, IAME, Paris, France.;Univ Paris Diderot, Paris, France..
    Improved Confidence Intervals and P-Values by Sampling from the Normalized Likelihood2015Ingår i: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 42, nr S1, s. S56-S57Artikel i tidskrift (Övrigt vetenskapligt)
  • 23.
    van Dijkman, Sven
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Ueckert, Sebastian
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Plan, Elodie L.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Karlsson, Mats O.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Differentiation and prognosis of healthy subjects, swedds and parkinson's patients using a multi-dimensional item response theory model2017Ingår i: Journal of the Neurological Sciences, ISSN 0022-510X, E-ISSN 1878-5883, Vol. 381, nr Supplement, s. 97-98Artikel i tidskrift (Övrigt vetenskapligt)
  • 24.
    Vong, Camille
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Ueckert, Sebastian
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Nyberg, Joakim
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Hooker, Andrew C.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.
    Handling Below Limit of Quantification Data in Optimal Trial Design2014Ingår i: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744Artikel i tidskrift (Övrigt vetenskapligt)
    Abstract [en]

    Methods that perform well in handling limit of quantification (LOQ) data exist in estimation of parameters for non-linear mixed effect models but are not well developed in experimental design.  The aim of this work was to evaluate existing methods and to explore new methods of handling LOQs in Optimal Design (OD). Seven different methods were implemented in PopED 2.13: D1 (Ignore LOQ), D2 (Non-informative Fisher information matrix (FIM) for median response below LOQ), new D3 (Non-informative FOCE linearized FIM for individual response below LOQ), D4 (Addition of a homoscedastic variance), new D5 (Simulation & Rescaling), new D6 (Integration & Rescaling) and new D7 (joint likelihood using the Laplace approximation). Predictive performance of D1-D7 was first assessed and sample time optimization was performed for a number of different LOQ levels. Resulting designs were evaluated for bias and imprecision, robustness and predictability from multiple stochastic simulations and estimations (SSE) in NONMEM using the M3 method. Evaluated determinants of the FIM for all methods, except D1 and D4, were in good agreement with SSE-derived covariance. In optimization, D6 provided the most accurate and precise parameter estimates and the designs with the best predictive performance under the M3 method. Methods D1 and D2 resulted in the least robust designs for estimation. Method D4 was shown to be insensitive to LOQ levels. For the scenarios investigated, method D6 showed the best compromise in terms of speed and accuracy. The use of OD methods anticipating LOQ data in planned designs allows better parameter estimation.

1 - 24 av 24
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