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Plan, Elodie L.
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Publications (10 of 26) Show all publications
Germovsek, E., Ambery, C., Yang, S., Beerahee, M., Karlsson, M. O. & Plan, E. L. (2019). 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 Patients. AAPS Journal, 21(4), Article ID UNSP 60.
Open this publication in new window or tab >>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 Patients
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2019 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 21, no 4, article id UNSP 60Article in journal (Refereed) Published
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.

Keywords
EXACT questionnaire, IRT, Markov model, NONMEM, PRO
National Category
Pharmacology and Toxicology
Identifiers
urn:nbn:se:uu:diva-383495 (URN)10.1208/s12248-019-0319-9 (DOI)000466176800001 ()31028495 (PubMedID)
Available from: 2019-05-17 Created: 2019-05-17 Last updated: 2019-05-17Bibliographically approved
Gottipati, G., Berges, A. C., Yang, S., Chen, C., Karlsson, M. & Plan, E. L. (2019). Item Response Model Adaptation for Analyzing Data from Different Versions of Parkinson's Disease Rating Scales. Pharmaceutical research, 36(9), Article ID 135.
Open this publication in new window or tab >>Item Response Model Adaptation for Analyzing Data from Different Versions of Parkinson's Disease Rating Scales
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2019 (English)In: Pharmaceutical research, ISSN 0724-8741, E-ISSN 1573-904X, Vol. 36, no 9, article id 135Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
SPRINGER/PLENUM PUBLISHERS, 2019
Keywords
Data integration, disease progression, item response theory, movement disorder society (sponsored revision) - Unified parkinson's disease rating scale, parkinson's disease
National Category
Neurology
Identifiers
urn:nbn:se:uu:diva-390377 (URN)10.1007/s11095-019-2668-6 (DOI)000475976900001 ()31317279 (PubMedID)
Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2019-08-12Bibliographically approved
Brekkan, A., Lopez-Lazaro, L., Plan, E. L., Nyberg, J., Kankanwadi, S. & Karlsson, M. (2019). Pharmacokinetic and Pharmacodynamic Sensitivity of Pegfilgrastim. AAPS Journal
Open this publication in new window or tab >>Pharmacokinetic and Pharmacodynamic Sensitivity of Pegfilgrastim
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2019 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416Article in journal (Refereed) Submitted
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-381432 (URN)
Available from: 2019-04-09 Created: 2019-04-09 Last updated: 2019-04-11
Jönsson, S., Yang, S., Chen, C., Plan, E. L. & Karlsson, M. O. (2018). Sample size for detection of drug effect using item level and total score models for Unified Parkinson's Disease Rating Scale data. Journal of Pharmacokinetics and Pharmacodynamics, 45, S106-S107
Open this publication in new window or tab >>Sample size for detection of drug effect using item level and total score models for Unified Parkinson's Disease Rating Scale data
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2018 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 45, p. S106-S107Article in journal, Meeting abstract (Other academic) Published
Place, publisher, year, edition, pages
SPRINGER/PLENUM PUBLISHERS, 2018
National Category
Pharmacology and Toxicology
Identifiers
urn:nbn:se:uu:diva-365110 (URN)000445374700235 ()
Available from: 2018-11-15 Created: 2018-11-15 Last updated: 2018-11-15Bibliographically approved
van Dijkman, S., Ueckert, S., Plan, E. L. & Karlsson, M. O. (2017). Differentiation and prognosis of healthy subjects, swedds and parkinson's patients using a multi-dimensional item response theory model. Paper presented at 23rd World Congress of Neurology (WCN), SEP 16-21, 2017, Kyoto, JAPAN. Journal of the Neurological Sciences, 381(Supplement), 97-98
Open this publication in new window or tab >>Differentiation and prognosis of healthy subjects, swedds and parkinson's patients using a multi-dimensional item response theory model
2017 (English)In: Journal of the Neurological Sciences, ISSN 0022-510X, E-ISSN 1878-5883, Vol. 381, no Supplement, p. 97-98Article in journal, Meeting abstract (Other academic) Published
National Category
Neurology
Identifiers
urn:nbn:se:uu:diva-356247 (URN)10.1016/j.jns.2017.08.317 (DOI)000427450300291 ()
Conference
23rd World Congress of Neurology (WCN), SEP 16-21, 2017, Kyoto, JAPAN
Note

Meeting Abstract: 291

Available from: 2018-07-27 Created: 2018-07-27 Last updated: 2018-07-27Bibliographically approved
van Dijkman, S., Gottipati, G., Plan, E. L. & Karlsson, M. O. (2017). Differentiation between Parkinson's disease patients and SWEDDs based on the MDS-UPDRS. Paper presented at 3rd Congress of the European-Academy-of-Neurology, JUN, 2017, Amsterdam, NETHERLANDS. European Journal of Neurology, 24(SI), 642-642
Open this publication in new window or tab >>Differentiation between Parkinson's disease patients and SWEDDs based on the MDS-UPDRS
2017 (English)In: European Journal of Neurology, ISSN 1351-5101, E-ISSN 1468-1331, Vol. 24, no SI, p. 642-642Article in journal, Meeting abstract (Other academic) Published
National Category
Neurology
Identifiers
urn:nbn:se:uu:diva-345641 (URN)000405530101332 ()
Conference
3rd Congress of the European-Academy-of-Neurology, JUN, 2017, Amsterdam, NETHERLANDS
Available from: 2018-03-12 Created: 2018-03-12 Last updated: 2018-03-12Bibliographically approved
Gottipati, G., Karlsson, M. O. & Plan, E. L. (2017). Modeling a Composite Score in Parkinson's Disease Using Item Response Theory. AAPS Journal, 19(3), 837-845
Open this publication in new window or tab >>Modeling a Composite Score in Parkinson's Disease Using Item Response Theory
2017 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 19, no 3, p. 837-845Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
SPRINGER, 2017
Keywords
Parkinson's disease, Item Response Theory, Movement Disorder Society (sponsored revision) Unified Parkinson's Disease Rating Scale, disease progression
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-323036 (URN)10.1208/s12248-017-0058-8 (DOI)000400565300024 ()28247193 (PubMedID)
Available from: 2017-06-09 Created: 2017-06-09 Last updated: 2018-01-13Bibliographically approved
Plan, E. L., Nyberg, J., Bauer, R. J. & Karlsson, M. O. (2015). Handling Underlying Discrete Variables with Mixed Hidden Markov Models in NONMEM. Paper presented at The American Conference on Pharmacometrics 2015 (ACoP6), October 3 to 8, 2015, Virginia, USA. Journal of Pharmacokinetics and Pharmacodynamics, 42(S1), S57-S57
Open this publication in new window or tab >>Handling Underlying Discrete Variables with Mixed Hidden Markov Models in NONMEM
2015 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 42, no S1, p. S57-S57Article in journal, Meeting abstract (Other academic) Published
National Category
Pharmacology and Toxicology
Identifiers
urn:nbn:se:uu:diva-280821 (URN)000367842000103 ()
Conference
The American Conference on Pharmacometrics 2015 (ACoP6), October 3 to 8, 2015, Virginia, USA
Note

Meeting Abstract: T-34

Available from: 2016-03-30 Created: 2016-03-15 Last updated: 2018-01-10Bibliographically approved
Swat, M. J., Moodie, S., Wimalaratne, S. M., Kristensen, N. R., Lavielle, M., Mari, A., . . . Le Novère, N. (2015). Pharmacometrics Markup Language (PharmML): Opening New Perspectives for Model Exchange in Drug Development. CPT pharmacometrics & systems pharmacology, 4(6), 316-319
Open this publication in new window or tab >>Pharmacometrics Markup Language (PharmML): Opening New Perspectives for Model Exchange in Drug Development
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2015 (English)In: CPT pharmacometrics & systems pharmacology, ISSN 2163-8306, Vol. 4, no 6, p. 316-319Article in journal (Refereed) Published
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.

National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-276254 (URN)10.1002/psp4.57 (DOI)26225259 (PubMedID)
Available from: 2016-02-10 Created: 2016-02-10 Last updated: 2018-01-10Bibliographically approved
Johansson, Å. M., Ueckert, S., Plan, E. L., Hooker, A. C. & Karlsson, M. O. (2014). Evaluation of Bias, Precision, Robustness and Runtime for Estimation Methods in NONMEM 7. Journal of Pharmacokinetics and Pharmacodynamics, 41(3), 223-238
Open this publication in new window or tab >>Evaluation of Bias, Precision, Robustness and Runtime for Estimation Methods in NONMEM 7
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2014 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 41, no 3, p. 223-238Article in journal (Refereed) 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.

Keywords
NONMEM, estimation algorithms
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-216136 (URN)10.1007/s10928-014-9359-z (DOI)000338496300003 ()24801864 (PubMedID)
Available from: 2014-01-19 Created: 2014-01-19 Last updated: 2018-01-11Bibliographically approved
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