uu.seUppsala University Publications
Change search
Refine search result
1 - 26 of 26
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1. Ahn, Jae Eun
    et al.
    Plan, Elodie L
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Miller, Raymond
    Modeling longitudinal daily seizure frequency data from pregabalin add-on treatment2012In: Journal of clinical pharmacology, ISSN 0091-2700, E-ISSN 1552-4604, Vol. 52, no 6, p. 880-892Article in journal (Refereed)
    Abstract [en]

    The purpose of this study was to describe longitudinal daily seizure count data with respect to the effects of time and pregabalin add-on therapy. Models were developed in step-wise manner: base model, time effect model, and time and drug effect (final) model, using a negative binomial distribution with Markovian features. Mean daily seizure count (λ) was estimated to be 0.385 (RSE 3.09%) and was further increased depending on the seizure count on the previous day. An overdispersion parameter (OVDP), representing extra-Poisson variation, was estimated to be 0.330 (RSE 11.7%). Inter-individual variances on λ and OVDP were 84.7% and 210%, respectively. Over time, λ tended to increase exponentially with a rate constant of 0.272 year-1 (RSE 26.8%). A mixture model was applied to classify responders/non-responders to pregabalin treatment. Within the responders, λ decreased exponentially with respect to dose with a constant of 0.00108 mg-1 (RSE 11.9%). The estimated responder rate was 66% (RSE 27.6%). Simulation-based diagnostics showed the model reasonably reproduced the characteristics of observed data. Highly variable daily seizure frequency was successfully characterized incorporating baseline characteristics, time effect, and the effect of pregabalin with classification of responders/non-responders, all of which are necessary to adequately assess the efficacy of antiepileptic drugs.

     

  • 2.
    Brekkan, Ari
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Jönsson, Siv
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Plan, Elodie L.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Handling Underlying Discrete Variables with Bivariate Mixed Hidden-Markov Models in NONMEMManuscript (preprint) (Other academic)
  • 3.
    Brekkan, Ari
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lledo-Garcia, Rocio
    Lacroix, Brigitte
    Jönsson, Siv
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Plan, Elodie L.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Characterization of Anti-Drug Antibody Dynamics Using a Bivariate Mixed Hidden-Markov ModelManuscript (preprint) (Other academic)
  • 4.
    Brekkan, Ari
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lopez-Lazaro, Luis
    Plan, Elodie L.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nyberg, Joakim
    Kankanwadi, Suresh
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Pharmacokinetic and Pharmacodynamic Sensitivity of Pegfilgrastim2019In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416Article in journal (Refereed)
  • 5.
    Germovsek, Eva
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ambery, Claire
    GlaxoSmithKline, Clin Pharmacol Modelling & Simulat, London, England.
    Yang, Shuying
    GlaxoSmithKline, Clin Pharmacol Modelling & Simulat, London, England.
    Beerahee, Misba
    GlaxoSmithKline, Clin Pharmacol Modelling & Simulat, London, England.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Plan, Elodie L.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    A Novel Method for Analysing Frequent Observations from Questionnaires in Order to Model Patient-Reported Outcomes: Application to EXACT (R) Daily Diary Data from COPD Patients2019In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 21, no 4, article id UNSP 60Article in journal (Refereed)
    Abstract [en]

    Chronic obstructive pulmonary disease (COPD) is a progressive lung disease with approximately 174 million cases worldwide. Electronic questionnaires are increasingly used for collecting patient-reported-outcome (PRO) data about disease symptoms. Our aim was to leverage PRO data, collected to record COPD disease symptoms, in a general modelling framework to enable interpretation of PRO observations in relation to disease progression and potential to predict exacerbations. The data were collected daily over a year, in a prospective, observational study. The e-questionnaire, the EXAcerbations of COPD Tool (EXACT (R)) included 14 items (i.e. questions) with 4 or 5 ordered categorical response options. An item response theory (IRT) model was used to relate the responses from each item to the underlying latent variable (which we refer to as disease severity), and on each item level, Markov models (MM) with 4 or 5 categories were applied to describe the dependence between consecutive observations. Minimal continuous time MMs were used and parameterised using ordinary differential equations. One hundred twenty-seven COPD patients were included (median age 67years, 54% male, 39% current smokers), providing approximately 40,000 observations per EXACT (R) item. The final model suggested that, with time, patients more often reported the same scores as the previous day, i.e. the scores were more stable. The modelled COPD disease severity change over time varied markedly between subjects, but was small in the typical individual. This is the first IRT model with Markovian properties; our analysis proved them necessary for predicting symptom-defined exacerbations.

  • 6.
    Gottipati, Gopichand
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Berges, Alienor C.
    GlaxoSmithKline, London, England;AstraZeneca, IMED Biotech Unit, Early Clin Dev, Quantitat Clin Pharmacol, Cambridge, England.
    Yang, Shuying
    GlaxoSmithKline, London, England.
    Chen, Chao
    GlaxoSmithKline, London, England.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Plan, Elodie L.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Item Response Model Adaptation for Analyzing Data from Different Versions of Parkinson's Disease Rating Scales2019In: Pharmaceutical research, ISSN 0724-8741, E-ISSN 1573-904X, Vol. 36, no 9, article id 135Article in journal (Refereed)
    Abstract [en]

    Purpose: The aim of this work was to allow combination of information from recent and historical trials in Parkinson's Disease (PD) by developing bridging methodology between two versions of the clinical endpoint.

    Methods: A previously developed Item Response Model (IRM), that described longitudinal changes in Movement Disorder Society (MDS) sponsored revision of Unified Parkinson's Disease Rating Scale (UPDRS) [MDS-UPDRS] data from the De Novo PD cohort in Parkinson's Progression Markers Initiative, was first adapted to describe baseline UPDRS data from two clinical trials, one in subjects with early PD and another in subjects with advanced PD. Assuming similar IRM structure, items of the UPDRS version were mapped to those in the MDS-UPDRS version. Subsequently, the longitudinal changes in the placebo arm of the advanced PD study were characterized.

    Results: The parameters reflecting differences in the shared items between endpoints were successfully estimated, and the model diagnostics indicated that mapping was better for early PD subjects (closer to De Novo cohort) than for advanced PD subjects. Disease progression for placebo in advanced PD patients was relatively shallow.

    Conclusion: An IRM able to handle two variants of clinical PD endpoints was developed; it can improve the utilization of data from diverse sources and diverse disease populations.

  • 7.
    Gottipati, Gopichand
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala Univ, Dept Pharmaceut Biosci, Box 591, S-75124 Uppsala, Sweden..
    Plan, Elodie L.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Modeling a Composite Score in Parkinson's Disease Using Item Response Theory2017In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 19, no 3, p. 837-845Article in journal (Refereed)
    Abstract [en]

    In the current work, we present the methodology for development of an Item Response Theory model within a non-linear mixed effects framework to characterize the longitudinal changes of the Movement Disorder Society (sponsored revision) of Unified Parkinson's Disease Rating Scale (MDS-UPDRS) endpoint in Parkinson's disease (PD). The data were obtained from Parkinson's Progression Markers Initiative database and included 163,070 observations up to 48 months from 430 subjects belonging to De Novo PD cohort. The probability of obtaining a score, reported for each of the items in the questionnaire, was modeled as a function of the subject's disability. Initially, a single latent variable model was explored to characterize the disease progression over time. However, based on the understanding of the questionnaire set-up and the results of a residuals-based diagnostic tool, a three latent variable model with a mixture implementation was able to adequately describe longitudinal changes not only at the total score level but also at each individual item level. The linear progression rates obtained for the patient-reported items and the non-sided items were similar, each of which roughly take about 50 months for a typical subject to progress linearly from the baseline by one standard deviation. However for the sided items, it was found that the better side deteriorates quicker than the disabled side. This study presents a framework for analyzing MDS-UPDRS data, which can be adapted to more traditional UPDRS data collected in PD clinical trials and result in more efficient designs and analyses of such studies.

  • 8.
    Johansson, Åsa M.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ueckert, Sebastian
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Plan, Elodie L.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Evaluation of Bias, Precision, Robustness and Runtime for Estimation Methods in NONMEM 72014In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 41, no 3, p. 223-238Article in journal (Refereed)
    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.

  • 9.
    Jönsson, Siv
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Yang, Shuying
    GlaxoSmithKline, London, England..
    Chen, Chao
    GlaxoSmithKline, London, England..
    Plan, Elodie L.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Sample size for detection of drug effect using item level and total score models for Unified Parkinson's Disease Rating Scale data2018In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 45, p. S106-S107Article in journal (Other academic)
  • 10.
    Karlsson, Kristin E.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Plan, Elodie L.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Performance of three estimation methods in repeated time-to-event modeling2011In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 13, no 1, p. 83-91Article in journal (Refereed)
    Abstract [en]

    It is not uncommon that the outcome measurements, symptoms or side effects, of a clinical trial belong to the family of event type data, e.g., bleeding episodes or emesis events. Event data is often low in information content and the mixed-effects modeling software NONMEM has previously been shown to perform poorly with low information ordered categorical data. The aim of this investigation was to assess the performance of the Laplace method, the stochastic approximation expectation-maximization (SAEM) method, and the importance sampling method when modeling repeated time-to-event data. The Laplace method already existed, whereas the two latter methods have recently become available in NONMEM 7. A stochastic simulation and estimation study was performed to assess the performance of the three estimation methods when applied to a repeated time-to-event model with a constant hazard associated with an exponential interindividual variability. Various conditions were investigated, ranging from rare to frequent events and from low to high interindividual variability. The method performance was assessed by parameter bias and precision. Due to the lack of information content under conditions where very few events were observed, all three methods exhibit parameter bias and imprecision, however most pronounced by the Laplace method. The performance of the SAEM and importance sampling were generally higher than Laplace when the frequency of individuals with events was less than 43%, while at frequencies above that all methods were equal in performance.

  • 11.
    Plan, Elodie L
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Modeling and simulation of count data2014In: CPT: pharmacometrics & systems pharmacology, ISSN 2163-8306, Vol. 3, no 8, p. 1-12Article in journal (Refereed)
    Abstract [en]

    Count data, or number of events per time interval, are discrete data arising from repeated time to event observations. Their mean count, or piecewise constant event rate, can be evaluated by discrete probability distributions from the Poisson model family. Clinical trial data characterization often involves population count analysis. This tutorial presents the basics and diagnostics of count modeling and simulation in the context of pharmacometrics. Consideration is given to overdispersion, underdispersion, autocorrelation, and inhomogeneity.

  • 12.
    Plan, Elodie L
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Elshoff, Jan-Peer
    Stockis, Armel
    Sargentini-Maier, Laura
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Likert Pain Scores Modeling: A Markov Integer Model and an Autoregressive Continuous Model2012In: Clinical Pharmacology and Therapeutics, ISSN 0009-9236, E-ISSN 1532-6535, Vol. 91, no 5, p. 820-828Article in journal (Refereed)
    Abstract [en]

    Pain intensity is principally assessed using rating scales such as the 11-point Likert scale. In general, frequent pain assessments are serially correlated and underdispersed. The aim of this investigation was to develop population models adapted to fit the 11-point pain scale. Daily Likert scores were recorded over 18 weeks by 231 patients with neuropathic pain from a clinical trial placebo group. An integer model consisting of a truncated generalized Poisson (GP) distribution with Markovian transition probability inflation was implemented in NONMEM 7.1.0. It was compared to a logit-transformed autoregressive continuous model with correlated residual errors. In both models, the score baseline was estimated to be 6.2 and the placebo effect to be 19%. Developed models similarly retrieved consistent underlying features of the data and therefore correspond to platform models for drug effect detection. The integer model was complex but flexible, whereas the continuous model can more easily be developed, although requires longer runtimes.

     

  • 13.
    Plan, Elodie L.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Kristin E.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Approaches to simultaneous analysis of frequency and severity of symptoms2010In: Clinical Pharmacology and Therapeutics, ISSN 0009-9236, E-ISSN 1532-6535, Vol. 88, no 2, p. 255-259Article in journal (Refereed)
    Abstract [en]

    Mechanistic models that synthesize pharmacological and (patho) physiological process information provide a rich basis for the characterization of drug action. However, the underlying clinical data are often simplified in a manner that does not allow models to fully elucidate the structure of the drug effect. In this article, we describe data-simplification strategies that are in routine use to describe disease symptoms and compare them with a model developed for handling the true complexities of the data.

  • 14.
    Plan, Elodie L
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ma, Guangli
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Någård, Mats
    Jensen, Jörgen
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Transient Lower Esophageal Sphincter Relaxations PKPD Modeling: Count Model and Repeated Time-To-Event Model2011In: Journal of Pharmacology and Experimental Therapeutics, ISSN 0022-3565, E-ISSN 1521-0103Article in journal (Refereed)
    Abstract [en]

    Transient lower esophageal sphincter relaxation (TLESR) is the major mechanism for gastro-esophageal reflux. Characterization of candidate compounds for reduction of TLESRs are traditionally done through summary exposure and response measures and would benefit from model-based analyses of exposure-TLESR events relationships. PKPD modeling approaches treating TLESR either as count data or as repeated time-to-event (RTTE) data were developed and compared in terms of ability to characterize system and drug characteristics. Vehicle data comprising 294 TLESR events were collected from 9 dogs. Compound (WIN55251-2) data containing 66 TLESR events, as well as plasma concentrations, were obtained from 4 dogs. Each experiment lasted for 45min and was initiated with a meal. Counts in equispaced 5-min intervals and 1-min intervals were modeled based on a Poisson probability distribution model. TLESR events were analyzed with the RTTE model. PK was connected to PD models with a 1-compartment model. Vehicle data were described by a baseline and a surge function; the surge peak was determined around 9.69min by all approaches and its width of 5min (1-min count and RTTE) or 10min (5-min count). TLESRs inhibition by WIN55251-2 was described by an Imax model, with an IC50 of on average 2.39nmol.L-1. Modeling approaches utilizing count or RTTE data linked to a dynamic PKPD representation of exposure is superior to using summary PK and PD measures. Differences in terms of predictions and power to detect a significant drug effect are illustrated with a simulation-based investigation, and a range of diagnostics for such modeling approaches is presented.

     

  • 15.
    Plan, Elodie L.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Maloney, Alan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Mentré, France
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Bertrand, Julie
    Performance comparison of various maximum likelihood nonlinear mixed-effects estimation methods for dose-response models2012In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 14, no 3, p. 420-432Article in journal (Refereed)
    Abstract [en]

    Estimation methods for nonlinear mixed-effects modelling have considerably improved over the last decades. Nowadays, several algorithms implemented in different software are used. The present study aimed at comparing their performance for dose–response models. Eight scenarios were considered using a sigmoid E max model, with varying sigmoidicity and residual error models. One hundred simulated datasets for each scenario were generated. One hundred individuals with observations at four doses constituted the rich design and at two doses, the sparse design. Nine parametric approaches for maximum likelihood estimation were studied: first-order conditional estimation (FOCE) in NONMEM and R, LAPLACE in NONMEM and SAS, adaptive Gaussian quadrature (AGQ) in SAS, and stochastic approximation expectation maximization (SAEM) in NONMEM and MONOLIX (both SAEM approaches with default and modified settings). All approaches started first from initial estimates set to the true values and second, using altered values. Results were examined through relative root mean squared error (RRMSE) of the estimates. With true initial conditions, full completion rate was obtained with all approaches except FOCE in R. Runtimes were shortest with FOCE and LAPLACE and longest with AGQ. Under the rich design, all approaches performed well except FOCE in R. When starting from altered initial conditions, AGQ, and then FOCE in NONMEM, LAPLACE in SAS, and SAEM in NONMEM and MONOLIX with tuned settings, consistently displayed lower RRMSE than the other approaches. For standard dose–response models analyzed through mixed-effects models, differences were identified in the performance of estimation methods available in current software, giving material to modellers to identify suitable approaches based on an accuracy-versus-runtime trade-off.

  • 16.
    Plan, Elodie L.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Maloney, Alan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Trocóniz, Iñaki F.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Performance in population models for count data, part I: maximum likelihood approximations.2009In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 36, no 4, p. 353-366Article in journal (Refereed)
    Abstract [en]

    There has been little evaluation of maximum likelihood approximation methods for non-linear mixed effects modelling of count data. The aim of this study was to explore the estimation accuracy of population parameters from six count models, using two different methods and programs. Simulations of 100 data sets were performed in NONMEM for each probability distribution with parameter values derived from a real case study on 551 epileptic patients. Models investigated were: Poisson (PS), Poisson with Markov elements (PMAK), Poisson with a mixture distribution for individual observations (PMIX), Zero Inflated Poisson (ZIP), Generalized Poisson (GP) and Negative Binomial (NB). Estimations of simulated datasets were completed with Laplacian approximation (LAPLACE) in NONMEM and LAPLACE/Gaussian Quadrature (GQ) in SAS. With LAPLACE, the average absolute value of the bias (AVB) in all models was 1.02% for fixed effects, and ranged 0.32-8.24% for the estimation of the random effect of the mean count (lambda). The random effect of the overdispersion parameter present in ZIP, GP and NB was underestimated (-25.87, -15.73 and -21.93% of relative bias, respectively). Analysis with GQ 9 points resulted in an improvement in these parameters (3.80% average AVB). Methods implemented in SAS had a lower fraction of successful minimizations, and GQ 9 points was considerably slower than 1 point. Simulations showed that parameter estimates, even when biased, resulted in data that were only marginally different from data simulated from the true model. Thus all methods investigated appear to provide useful results for the investigated count data models.

  • 17.
    Plan, Elodie L.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nyberg, Joakim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Bauer, Robert J.
    ICON Dev Solut, R&D, Pharmacometr, Baltimore, MD USA..
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Handling Underlying Discrete Variables with Mixed Hidden Markov Models in NONMEM2015In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 42, no S1, p. S57-S57Article in journal (Other academic)
  • 18.
    Plan, Elodie L
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Sun, Yang
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Approaches to Likert-type Ordered Categorical Data AnalysisManuscript (preprint) (Other academic)
    Abstract [en]

    Purpose: Ordinal variables are common pharmacodynamic responses, but when the number of categories reaches 11, current approaches to fit this type of data may not be suitable. This article aims at exploring modeling approach candidates for 11-point ordered categorical data.

    Methods: The study consisted of two sets of 100 stochastic simulations and estimations in NONMEM VI. A first set was generated with a baseline response ordered categorical model, whose parameter values were derived from a real case study with 100 observations on average per individual. A second set included a linear drug effect observed in a 4 parallel dose arm treatment trial. Simulated data were analyzed with an ordered categorical (OC), a truncated generalized Poisson (PO), and a logit-transformed continuous (CO) models.

    Results: The dose-response OC model needed 13 parameters, compared to 7 and 6 with the other models. Score distributions were closely mimicked by OC resimulations at all dose levels. The agreement with PO resimulated scores was best at low doses, in contrast to CO showing less discrepancy at high doses.

    Conclusions: Truncated count (PO) or logit-transformed continuous (CO) models may be alternatives to ordered categorical models for Likert scores in case of sparse data or in presence of serial correlations.

     

  • 19. Swat, M J
    et al.
    Moodie, S
    Wimalaratne, S M
    Kristensen, N R
    Lavielle, M
    Mari, A
    Magni, P
    Smith, M K
    Bizzotto, R
    Pasotti, L
    Mezzalana, E
    Comets, E
    Sarr, C
    Terranova, N
    Blaudez, E
    Chan, P
    Chard, J
    Chatel, K
    Chenel, M
    Edwards, D
    Franklin, C
    Giorgino, T
    Glont, M
    Girard, P
    Grenon, P
    Harling, Kajsa
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kaye, R
    Keizer, R
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kloft, C
    Kok, J N
    Kokash, N
    Laibe, C
    Laveille, C
    Lestini, G
    Mentré, F
    Munafo, A
    Nordgren, R
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nyberg, Henrik Bjugård
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Parra-Guillen, Z P
    Plan, Elodie L.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ribba, B
    Smith, G
    Trocóniz, I F
    Yvon, F
    Milligan, P A
    Harnisch, L
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hermjakob, H
    Le Novère, N
    Pharmacometrics Markup Language (PharmML): Opening New Perspectives for Model Exchange in Drug Development2015In: CPT pharmacometrics & systems pharmacology, ISSN 2163-8306, Vol. 4, no 6, p. 316-319Article in journal (Refereed)
    Abstract [en]

    The lack of a common exchange format for mathematical models in pharmacometrics has been a long-standing problem. Such a format has the potential to increase productivity and analysis quality, simplify the handling of complex workflows, ensure reproducibility of research, and facilitate the reuse of existing model resources. Pharmacometrics Markup Language (PharmML), currently under development by the Drug Disease Model Resources (DDMoRe) consortium, is intended to become an exchange standard in pharmacometrics by providing means to encode models, trial designs, and modeling steps.

  • 20.
    Trocóniz, Iñaki F
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Plan, Elodie L.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Miller, Raymond
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Modelling overdispersion and Markovian features in count data.2009In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 36, no 5, p. 461-477Article in journal (Refereed)
    Abstract [en]

    The number of counts (events) per unit of time is a discrete response variable that is generally analyzed with the Poisson distribution (PS) model. The PS model makes two assumptions: the mean number of counts (lambda) is assumed equal to the variance, and counts occurring in non-overlapping intervals are assumed independent. However, many counting outcomes show greater variability than predicted by the PS model, a phenomenon called overdispersion. The purpose of this study was to implement and explore, in the population context, different distribution models accounting for overdispersion and Markov patterns in the analysis of count data. Daily seizures count data obtained from 551 subjects during the 12-week screening phase of a double-blind, placebo-controlled, parallel-group multicenter study performed in epileptic patients with medically refractory partial seizures, were used in the current investigation. The following distribution models were fitted to the data: PS, Zero-Inflated PS (ZIP), Negative Binomial (NB), and Zero-Inflated Negative Binomial (ZINB) models. Markovian features were introduced estimating different lambdas and overdispersion parameters depending on whether the previous day was a seizure or a non-seizure day. All analyses were performed with NONMEM VI. All models were successfully implemented and all overdispersed models improved the fit with respect to the PS model. The NB model resulted in the best description of the data. The inclusion of Markovian features in lambda and in the overdispersion parameter improved the fit significantly (P < 0.001). The plot of the variance versus mean daily seizure count profiles, and the number of transitions, are suggested as model performance tools reflecting the capability to handle overdispersion and Markovian features, respectively.

  • 21.
    Ueckert, Sebastian
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Plan, Elodie L.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ito, Kaori
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Corrigan, Brian
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Benefits of an Item Response Theory Based Analysis of ADAS-cog Assessments2013In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 40, no S1, p. S49-S49Article in journal (Other academic)
  • 22.
    Ueckert, Sebastian
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Plan, Elodie L.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ito, Kaori
    Pfizer Inc.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Corrigan, Brian
    Pfizer Inc.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Improved Utilization of ADAS-cog Assessment Data through Item Response Theory based Pharmacometric Modeling2014In: Pharmaceutical research, ISSN 0724-8741, E-ISSN 1573-904X, Vol. 31, no 8, p. 2152-2165Article in journal (Refereed)
    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.

  • 23.
    Ueckert, Sebastian
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Plan, Elodie L.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ito, Kaori
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Corrigan, Brian
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Pharmacometric Modeling of Clinical ADAS-cog Assessment Data using Item Response Theory2013In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 40, no S1, p. S92-S92Article in journal (Other academic)
  • 24.
    Ueckert, Sebastian
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Plan, Elodie L.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ito, Kaori
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Corrigan, Brian
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Predicting Baseline ADAS-cog Scores from Screening Information using Item Response Theory and Full Random Effect Covariate Modeling2013In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 40, no S1, p. S71-S72Article in journal (Other academic)
  • 25.
    van Dijkman, Sven
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Gottipati, Gopichand
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Plan, Elodie L.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Differentiation between Parkinson's disease patients and SWEDDs based on the MDS-UPDRS2017In: European Journal of Neurology, ISSN 1351-5101, E-ISSN 1468-1331, Vol. 24, no SI, p. 642-642Article in journal (Other academic)
  • 26.
    van Dijkman, Sven
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ueckert, Sebastian
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Plan, Elodie L.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Differentiation and prognosis of healthy subjects, swedds and parkinson's patients using a multi-dimensional item response theory model2017In: Journal of the Neurological Sciences, ISSN 0022-510X, E-ISSN 1878-5883, Vol. 381, no Supplement, p. 97-98Article in journal (Other academic)
1 - 26 of 26
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf