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1دورية أكاديمية
المؤلفون: Ines Vandekerckhove, Marleen Van den Hauwe, Nathalie De Beukelaer, Elze Stoop, Marije Goudriaan, Margaux Delporte, Geert Molenberghs, Anja Van Campenhout, Liesbeth De Waele, Nathalie Goemans, Friedl De Groote, Kaat Desloovere
المصدر: Frontiers in Human Neuroscience, Vol 16 (2022)
مصطلحات موضوعية: Duchenne muscular dystrophy, typically developing children, three-dimensional gait analysis, gait pattern, longitudinal study, mixed models for repeated measures, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
الوصف: Prolonging ambulation is an important treatment goal in children with Duchenne muscular dystrophy (DMD). Three-dimensional gait analysis (3DGA) could provide sensitive parameters to study the efficacy of clinical trials aiming to preserve ambulation. However, quantitative descriptions of the natural history of gait features in DMD are first required. The overall goal was to provide a full delineation of the progressive gait pathology in children with DMD, covering the entire period of ambulation, by performing a so-called mixed cross-sectional longitudinal study. Firstly, to make our results comparable with previous literature, we aimed to cross-sectionally compare 31 predefined gait features between children with DMD and a typically developing (TD) database (1). Secondly, we aimed to explore the longitudinal changes in the 31 predefined gait features in growing boys with DMD using follow-up 3DGA sessions (2). 3DGA-sessions (n = 124) at self-selected speed were collected in 27 boys with DMD (baseline age: 4.6–15 years). They were repeatedly measured over a varying follow-up period (range: 6 months–5 years). The TD group consisted of 27 children (age: 5.4–15.6 years). Per measurement session, the spatiotemporal parameters, and the kinematic and kinetic waveforms were averaged over the selected gait cycles. From the averaged waveforms, discrete gait features (e.g., maxima and minima) were extracted. Mann-Whitney U tests were performed to cross-sectionally analyze the differences between DMD at baseline and TD (1). Linear mixed effect models were performed to assess the changes in gait features in the same group of children with DMD from both a longitudinal (i.e., increasing time) as well as a cross-sectional perspective (i.e., increasing baseline age) (2). At baseline, the boys with DMD differed from the TD children in 17 gait features. Additionally, 21 gait features evolved longitudinally when following-up the same boys with DMD and 25 gait features presented a significant cross-sectional baseline age-effect. The current study quantitatively described the longitudinal alterations in gait features in boys with DMD, thereby providing detailed insight into how DMD gait deteriorates. Additionally, our results highlight that gait features extracted from 3DGA are promising outcome measures for future clinical trials to quantify the efficacy of novel therapeutic strategies.
وصف الملف: electronic resource
العلاقة: https://www.frontiersin.org/articles/10.3389/fnhum.2022.861136/fullTest; https://doaj.org/toc/1662-5161Test
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2
المؤلفون: Wellhagen, Gustaf J., Hamren, Bengt, Kjellsson, Maria C., docent, 1975, Astrand, Magnus
المصدر: Pharmaceutical research. 37(8)
مصطلحات موضوعية: Mixed models for repeated measures (MMRM), dose-response analysis, dose-response mixed models for repeated measures (DR-MMRM), chronic kidney disease (CKD), urinary albumin-to-creatinine ratio (UACR)
الوصف: Purpose In this paper we investigated a new method for dose-response analysis of longitudinal data in terms of precision and accuracy using simulations. Methods The new method, called Dose-Response Mixed Models for Repeated Measures (DR-MMRM), combines conventional Mixed Models for Repeated Measures (MMRM) and dose-response modeling. Conventional MMRM can be applied for highly variable repeated measure data and is a way to estimate the drug effect at each visit and dose, however without any assumptions regarding the dose-response shape. Dose-response modeling, on the other hand, utilizes information across dose arms and describes the drug effect as a function of dose. Drug development in chronic kidney disease (CKD) is complicated by many factors, primarily by the slow progression of the disease and lack of predictive biomarkers. Recently, new approaches and biomarkers are being explored to improve efficiency in CKD drug development. Proteinuria, i.e. urinary albumin-to-creatinine ratio (UACR) is increasingly used in dose finding trials in patients with CKD. We use proteinuria to illustrate the benefits of DR-MMRM. Results The DR-MMRM had higher precision than conventional MMRM and less bias than a dose-response model on UACR change from baseline to end-of-study (DR-EOS). Conclusions DR-MMRM is a promising method for dose-response analysis.
وصف الملف: electronic
الوصول الحر: https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-420868Test
https://doi.org/10.1007/s11095-020-02882-0Test
https://uu.diva-portal.org/smash/get/diva2:1472705/FULLTEXT01.pdfTest -
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المؤلفون: Wellhagen, Gustaf, 1988
المساهمون: Kjellsson, Maria C., docent, 1975, Hamrén, Bengt, Dr., Olsson Gisleskog, Per, Dr.
المصدر: Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy.
مصطلحات موضوعية: pharmacometrics, modelling, non-linear mixed effects modelling, composite scale data, total score data, total score analysis, precision prediction, bounded integer, bounded integer model, IRT-informed total score analysis, item response theory-informed, mixed models for repeated measures, MMRM, dose-response, dose-response mixed models for repeated measures, DR-MMRM, HbA1c models, fit-for-purpose analysis, clinical trial analysis, Farmaceutisk vetenskap, Pharmaceutical Science
الوصف: Clinical trials are needed to evaluate new treatments. In late-stage clinical trials, failures are mostly due to lack of efficacy. Fit-for-purpose analysis methods will likely increase the success rates and advance drug development by providing higher precision to support decisions such as go/no-go, dose selection, or sample size. This thesis presents new methods for analysis of composite scale data, and comparisons of prediction precision of new and standard analysis methods. Composite scale data arise from questions/items rated with integers. A total score can be derived, which is discrete and bounded. Item response theory (IRT) models are the natural choice for such data, since they use the item-level information. However, when only the total score is available they cannot be used. The bounded integer (BI) model is a new method for discrete, bounded outcomes. With composite scale total score data, it had superior fit compared to standard methods, because it respects the nature of the data. Further, a new method, formally linking IRT models to models for total score, was developed. The expected mean and variance, given an IRT model, was implemented in BI and continuous variable models. This improved fit, allowed estimation of IRT parameters, and allowed comparison of different model types.The prediction precision of both outcome and parameters were investigated with different methods, ranging from t-test to mechanistic pharmacometric models, for composite scale and continuous data. The most suitable method depended on the purpose, for example mechanistic models are superior at establishing a drug’s site of action.In conclusion, the choice of method should be based on the primary question, and also the data collected. The method should not be more complex than necessary, and the nature of the data respected. This thesis will help modellers select the most appropriate analysis method for a problem at hand.
وصف الملف: electronic
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4دورية أكاديمية
المؤلفون: Vandekerckhove, Ines, Van den Hauwe, Marleen, De Beukelaer, Nathalie, Stoop, Elze, Goudriaan, Marije, Delporte, Margaux, Molenberghs, Geert, Van Campenhout, Anja, De Waele, Liesbeth, Goemans, Nathalie, De Groote, Friedl, Desloovere, Kaat
المصدر: Vandekerckhove , I , Van den Hauwe , M , De Beukelaer , N , Stoop , E , Goudriaan , M , Delporte , M , Molenberghs , G , Van Campenhout , A , De Waele , L , Goemans , N , De Groote , F & Desloovere , K 2022 , ' Longitudinal Alterations in Gait Features in Growing Children With Duchenne Muscular Dystrophy ' , Frontiers in Human Neuroscience , vol. 16 , no. June , 861136 , pp. 1-20 . https://doi.org/10.3389/fnhum.2022.861136Test
مصطلحات موضوعية: Duchenne muscular dystrophy, gait pattern, longitudinal study, mixed models for repeated measures, three-dimensional gait analysis, typically developing children
الوصف: Prolonging ambulation is an important treatment goal in children with Duchenne muscular dystrophy (DMD). Three-dimensional gait analysis (3DGA) could provide sensitive parameters to study the efficacy of clinical trials aiming to preserve ambulation. However, quantitative descriptions of the natural history of gait features in DMD are first required. The overall goal was to provide a full delineation of the progressive gait pathology in children with DMD, covering the entire period of ambulation, by performing a so-called mixed cross-sectional longitudinal study. Firstly, to make our results comparable with previous literature, we aimed to cross-sectionally compare 31 predefined gait features between children with DMD and a typically developing (TD) database (1). Secondly, we aimed to explore the longitudinal changes in the 31 predefined gait features in growing boys with DMD using follow-up 3DGA sessions (2). 3DGA-sessions (n = 124) at self-selected speed were collected in 27 boys with DMD (baseline age: 4.6–15 years). They were repeatedly measured over a varying follow-up period (range: 6 months–5 years). The TD group consisted of 27 children (age: 5.4–15.6 years). Per measurement session, the spatiotemporal parameters, and the kinematic and kinetic waveforms were averaged over the selected gait cycles. From the averaged waveforms, discrete gait features (e.g., maxima and minima) were extracted. Mann-Whitney U tests were performed to cross-sectionally analyze the differences between DMD at baseline and TD (1). Linear mixed effect models were performed to assess the changes in gait features in the same group of children with DMD from both a longitudinal (i.e., increasing time) as well as a cross-sectional perspective (i.e., increasing baseline age) (2). At baseline, the boys with DMD differed from the TD children in 17 gait features. Additionally, 21 gait features evolved longitudinally when following-up the same boys with DMD and 25 gait features presented a significant cross-sectional baseline age-effect. The ...
الإتاحة: https://doi.org/10.3389/fnhum.2022.861136Test
https://research.vu.nl/en/publications/25e5db9f-39ab-4f81-a78f-61fb517b2fffTest
https://hdl.handle.net/1871.1/25e5db9f-39ab-4f81-a78f-61fb517b2fffTest
http://www.scopus.com/inward/record.url?scp=85132839121&partnerID=8YFLogxKTest
http://www.scopus.com/inward/citedby.url?scp=85132839121&partnerID=8YFLogxKTest -
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المؤلفون: Wellhagen, Gustaf J., 1988, Karlsson, Mats O., Kjellsson, Maria C., docent, 1975
المصدر: AAPS Journal. 23(1)
مصطلحات موضوعية: Composite scale data, total score data, bounded integer model, mixed models for repeated measures, IRT-informed total score analysis, Pharmaceutical Science, Farmaceutisk vetenskap
الوصف: Total score (TS) data is generated from composite scales consisting of several questions/items, such as the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). The analysis method that most fully use the information gathered is item response theory (IRT) models, but these are complex and require item-level data which may not be available. Therefore, the TS is commonly analysed with standard continuous variable (CV) models, which do not respect the bounded nature of data. Bounded integer (BI) models do respect the data nature but are not as extensively researched. Mixed models for repeated measures (MMRM) are an alternative that requires few assumptions and handles dropout without bias. If an IRT model exists, the expected mean and standard deviation of TS can be computed through IRT-informed functions – which allows CV and BI models to estimate parameters on the IRT scale. The fit, performance on external data, and parameter precision (when applicable) of CV, BI and MMRM to analyse simulated TS data from the MDS-UPDRS motor subscale is investigated in this work. All models provided accurate predictions and residuals without trends, but the fit of CV and BI models was improved by IRT-informed functions. The IRT-informed BI model had more precise parameter estimates than the IRT-informed CV model. The IRT-informed models also had the best performance on external data, while the MMRM model was worst. In conclusion: 1) IRT-informed functions improve TS-analyses and 2) IRT-informed BI models had more precise IRT parameter estimates than IRT-informed CV models.
وصف الملف: electronic
الوصول الحر: https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-425806Test
https://doi.org/10.1208/s12248-020-00546-wTest
https://uu.diva-portal.org/smash/get/diva2:1502263/FULLTEXT01.pdfTest -
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المؤلفون: Ines Vandekerckhove, Marleen Van den Hauwe, Nathalie De Beukelaer, Elze Stoop, Marije Goudriaan, Margaux Delporte, Geert Molenberghs, Anja Van Campenhout, Liesbeth De Waele, Nathalie Goemans, Friedl De Groote, Kaat Desloovere
مصطلحات موضوعية: Neuroscience, Exercise Physiology, Central Nervous System, Computer Perception, Memory and Attention, Decision Making, Neurocognitive Patterns and Neural Networks, Music Therapy, Duchenne muscular dystrophy, typically developing children, three-dimensional gait analysis, gait pattern, longitudinal study, mixed models for repeated measures
الوصف: Prolonging ambulation is an important treatment goal in children with Duchenne muscular dystrophy (DMD). Three-dimensional gait analysis (3DGA) could provide sensitive parameters to study the efficacy of clinical trials aiming to preserve ambulation. However, quantitative descriptions of the natural history of gait features in DMD are first required. The overall goal was to provide a full delineation of the progressive gait pathology in children with DMD, covering the entire period of ambulation, by performing a so-called mixed cross-sectional longitudinal study. Firstly, to make our results comparable with previous literature, we aimed to cross-sectionally compare 31 predefined gait features between children with DMD and a typically developing (TD) database (1). Secondly, we aimed to explore the longitudinal changes in the 31 predefined gait features in growing boys with DMD using follow-up 3DGA sessions (2). 3DGA-sessions (n = 124) at self-selected speed were collected in 27 boys with DMD (baseline age: 4.6–15 years). They were repeatedly measured over a varying follow-up period (range: 6 months–5 years). The TD group consisted of 27 children (age: 5.4–15.6 years). Per measurement session, the spatiotemporal parameters, and the kinematic and kinetic waveforms were averaged over the selected gait cycles. From the averaged waveforms, discrete gait features (e.g., maxima and minima) were extracted. Mann-Whitney U tests were performed to cross-sectionally analyze the differences between DMD at baseline and TD (1). Linear mixed effect models were performed to assess the changes in gait features in the same group of children with DMD from both a longitudinal (i.e., increasing time) as well as a cross-sectional perspective (i.e., increasing baseline age) (2). At baseline, the boys with DMD differed from the TD children in 17 gait features. Additionally, 21 gait features evolved longitudinally when following-up the same boys with DMD and 25 gait features presented a significant cross-sectional baseline age-effect. The ...
الإتاحة: https://doi.org/10.3389/fnhum.2022.861136.s001Test
https://figshare.com/articles/dataset/Data_Sheet_1_Longitudinal_Alterations_in_Gait_Features_in_Growing_Children_With_Duchenne_Muscular_Dystrophy_docx/19956158Test -
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المؤلفون: Gustaf J Wellhagen, Magnus Åstrand, Maria C. Kjellsson, Bengt Hamrén
المصدر: Pharmaceutical Research
مصطلحات موضوعية: Mixed model, Oncology, medicine.medical_specialty, Time Factors, Pharmaceutical Science, chronic kidney disease (CKD), 030226 pharmacology & pharmacy, 03 medical and health sciences, Dose finding, 0302 clinical medicine, Bias, Slow progression, Albumins, Internal medicine, Urologi och njurmedicin, medicine, Humans, Urology and Nephrology, Computer Simulation, Pharmacology (medical), In patient, 030212 general & internal medicine, Renal Insufficiency, Chronic, Predictive biomarker, Pharmacology, Models, Statistical, Dose-Response Relationship, Drug, business.industry, Organic Chemistry, Repeated measures design, medicine.disease, dose-response analysis, dose-response mixed models for repeated measures (DR-MMRM), Treatment Outcome, Drug development, Creatinine, Data Interpretation, Statistical, Molecular Medicine, business, Mixed models for repeated measures (MMRM), Biomarkers, Research Paper, Biotechnology, Kidney disease, urinary albumin-to-creatinine ratio (UACR)
الوصف: PurposeIn this paper we investigated a new method for dose-response analysis of longitudinal data in terms of precision and accuracy using simulations.MethodsThe new method, called Dose-Response Mixed Models for Repeated Measures (DR-MMRM), combines conventional Mixed Models for Repeated Measures (MMRM) and dose-response modeling. Conventional MMRM can be applied for highly variable repeated measure data and is a way to estimate the drug effect at each visit and dose, however without any assumptions regarding the dose-response shape. Dose-response modeling, on the other hand, utilizes information across dose arms and describes the drug effect as a function of dose. Drug development in chronic kidney disease (CKD) is complicated by many factors, primarily by the slow progression of the disease and lack of predictive biomarkers. Recently, new approaches and biomarkers are being explored to improve efficiency in CKD drug development. Proteinuria, i.e. urinary albumin-to-creatinine ratio (UACR) is increasingly used in dose finding trials in patients with CKD. We use proteinuria to illustrate the benefits of DR-MMRM.ResultsThe DR-MMRM had higher precision than conventional MMRM and less bias than a dose-response model on UACR change from baseline to end-of-study (DR-EOS).ConclusionsDR-MMRM is a promising method for dose-response analysis.
وصف الملف: application/pdf
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a1490e7832ab66baa589ec317447cf91Test
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-420868Test -
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المؤلفون: Maria C. Kjellsson, Mats O. Karlsson, Gustaf J. Wellhagen
المصدر: The AAPS Journal
مصطلحات موضوعية: Mixed model, Accuracy and precision, Scale (ratio), composite scale data, Pharmaceutical Science, Severity of Illness Index, 030226 pharmacology & pharmacy, 01 natural sciences, Standard deviation, 010104 statistics & probability, 03 medical and health sciences, Pharmaceutical Sciences, 0302 clinical medicine, Item response theory, Statistics, Humans, 0101 mathematics, Mathematics, Models, Statistical, Repeated measures design, Parkinson Disease, Farmaceutiska vetenskaper, IRT-informed total score analysis, Composite scale data, Treatment Outcome, Data Interpretation, Statistical, Bounded function, mixed models for repeated measures, total score data, bounded integer model, Research Article, Integer (computer science)
الوصف: Total score (TS) data is generated from composite scales consisting of several questions/items, such as the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). The analysis method that most fully uses the information gathered is item response theory (IRT) models, but these are complex and require item-level data which may not be available. Therefore, the TS is commonly analysed with standard continuous variable (CV) models, which do not respect the bounded nature of data. Bounded integer (BI) models do respect the data nature but are not as extensively researched. Mixed models for repeated measures (MMRM) are an alternative that requires few assumptions and handles dropout without bias. If an IRT model exists, the expected mean and standard deviation of TS can be computed through IRT-informed functions—which allows CV and BI models to estimate parameters on the IRT scale. The fit, performance on external data and parameter precision (when applicable) of CV, BI and MMRM to analyse simulated TS data from the MDS-UPDRS motor subscale are investigated in this work. All models provided accurate predictions and residuals without trends, but the fit of CV and BI models was improved by IRT-informed functions. The IRT-informed BI model had more precise parameter estimates than the IRT-informed CV model. The IRT-informed models also had the best performance on external data, while the MMRM model was worst. In conclusion, (1) IRT-informed functions improve TS analyses and (2) IRT-informed BI models had more precise IRT parameter estimates than IRT-informed CV models. Supplementary Information The online version contains supplementary material available at 10.1208/s12248-020-00546-w.
وصف الملف: application/pdf
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dc35c4717ba7aa2b527f52934e223b9cTest
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-425806Test -
9مورد إلكتروني
مستخلص: Purpose In this paper we investigated a new method for dose-response analysis of longitudinal data in terms of precision and accuracy using simulations. Methods The new method, called Dose-Response Mixed Models for Repeated Measures (DR-MMRM), combines conventional Mixed Models for Repeated Measures (MMRM) and dose-response modeling. Conventional MMRM can be applied for highly variable repeated measure data and is a way to estimate the drug effect at each visit and dose, however without any assumptions regarding the dose-response shape. Dose-response modeling, on the other hand, utilizes information across dose arms and describes the drug effect as a function of dose. Drug development in chronic kidney disease (CKD) is complicated by many factors, primarily by the slow progression of the disease and lack of predictive biomarkers. Recently, new approaches and biomarkers are being explored to improve efficiency in CKD drug development. Proteinuria, i.e. urinary albumin-to-creatinine ratio (UACR) is increasingly used in dose finding trials in patients with CKD. We use proteinuria to illustrate the benefits of DR-MMRM. Results The DR-MMRM had higher precision than conventional MMRM and less bias than a dose-response model on UACR change from baseline to end-of-study (DR-EOS). Conclusions DR-MMRM is a promising method for dose-response analysis.
مصطلحات الفهرس: Mixed models for repeated measures (MMRM), dose-response analysis, dose-response mixed models for repeated measures (DR-MMRM), chronic kidney disease (CKD), urinary albumin-to-creatinine ratio (UACR), Urology and Nephrology, Urologi och njurmedicin, Article in journal, info:eu-repo/semantics/article, text
URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-420868Test
Pharmaceutical research, 0724-8741, 2020, 37:8 -
10مورد إلكتروني
مستخلص: Clinical trials are needed to evaluate new treatments. In late-stage clinical trials, failures are mostly due to lack of efficacy. Fit-for-purpose analysis methods will likely increase the success rates and advance drug development by providing higher precision to support decisions such as go/no-go, dose selection, or sample size. This thesis presents new methods for analysis of composite scale data, and comparisons of prediction precision of new and standard analysis methods. Composite scale data arise from questions/items rated with integers. A total score can be derived, which is discrete and bounded. Item response theory (IRT) models are the natural choice for such data, since they use the item-level information. However, when only the total score is available they cannot be used. The bounded integer (BI) model is a new method for discrete, bounded outcomes. With composite scale total score data, it had superior fit compared to standard methods, because it respects the nature of the data. Further, a new method, formally linking IRT models to models for total score, was developed. The expected mean and variance, given an IRT model, was implemented in BI and continuous variable models. This improved fit, allowed estimation of IRT parameters, and allowed comparison of different model types. The prediction precision of both outcome and parameters were investigated with different methods, ranging from t-test to mechanistic pharmacometric models, for composite scale and continuous data. The most suitable method depended on the purpose, for example mechanistic models are superior at establishing a drug’s site of action. In conclusion, the choice of method should be based on the primary question, and also the data collected. The method should not be more complex than necessary, and the nature of the data respected. This thesis will help modellers select the most appropriate analysis method for a problem at hand.
Zoom link: https://uu-se.zoom.us/j/63922730946Test Passcode: 210115مصطلحات الفهرس: pharmacometrics, modelling, non-linear mixed effects modelling, composite scale data, total score data, total score analysis, precision prediction, bounded integer, bounded integer model, IRT-informed total score analysis, item response theory-informed, mixed models for repeated measures, MMRM, dose-response, dose-response mixed models for repeated measures, DR-MMRM, HbA1c models, fit-for-purpose analysis, clinical trial analysis, Pharmaceutical Sciences, Farmaceutiska vetenskaper, Doctoral thesis, comprehensive summary, info:eu-repo/semantics/doctoralThesis, text
URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-425807Test
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, 1651-6192 ; 289
info:eu-repo/grantAgreement/EC/FP7/2013-602552
info:eu-repo/grantAgreement/EC/FP7/115156
النص الكامل 