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153 results on '"Vandemeulebroecke, Marc"'

1. WATCH: A Workflow to Assess Treatment Effect Heterogeneity in Drug Development for Clinical Trial Sponsors

Author

Sechidis, Konstantinos, Sun, Sophie, Chen, Yao, Lu, Jiarui, Zang, Cong, Baillie, Mark, Ohlssen, David, Vandemeulebroecke, Marc, Hemmings, Rob, Ruberg, Stephen, and Bornkamp, Björn

Subjects
Statistics - Applications
Abstract

This paper proposes a Workflow for Assessing Treatment effeCt Heterogeneity (WATCH) in clinical drug development targeted at clinical trial sponsors. The workflow is designed to address the challenges of investigating treatment effect heterogeneity (TEH) in randomized clinical trials, where sample size and multiplicity limit the reliability of findings. The proposed workflow includes four steps: Analysis Planning, Initial Data Analysis and Analysis Dataset Creation, TEH Exploration, and Multidisciplinary Assessment. The workflow aims to provide a systematic approach to explore treatment effect heterogeneity in the exploratory setting, taking into account external evidence and best scientific understanding.

Published
2024

2. Coping with Information Loss and the Use of Auxiliary Sources of Data: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions

Author

Calderazzo, Silvia, Tarima, Sergey, Reid, Carissa, Flournoy, Nancy, Friede, Tim, Geller, Nancy, Rosenberger, James L, Stallard, Nigel, Ursino, Moreno, Vandemeulebroecke, Marc, Van Lancker, Kelly, and Zohar, Sarah

Subjects
Statistics - Applications
Abstract

Clinical trials disruption has always represented a non negligible part of the ending of interventional studies. While the SARS-CoV-2 (COVID-19) pandemic has led to an impressive and unprecedented initiation of clinical research, it has also led to considerable disruption of clinical trials in other disease areas, with around 80% of non-COVID-19 trials stopped or interrupted during the pandemic. In many cases the disrupted trials will not have the planned statistical power necessary to yield interpretable results. This paper describes methods to compensate for the information loss arising from trial disruptions by incorporating additional information available from auxiliary data sources. The methods described include the use of auxiliary data on baseline and early outcome data available from the trial itself and frequentist and Bayesian approaches for the incorporation of information from external data sources. The methods are illustrated by application to the analysis of artificial data based on the Primary care pediatrics Learning Activity Nutrition (PLAN) study, a clinical trial assessing a diet and exercise intervention for overweight children, that was affected by the COVID-19 pandemic. We show how all of the methods proposed lead to an increase in precision relative to use of complete case data only.

Published
2022

3. A framework for longitudinal latent factor modelling of treatment response in clinical trials with applications to Psoriatic Arthritis and Rheumatoid Arthritis

Author

Falck, Fabian, Zhu, Xuan, Ghalebikesabi, Sahra, Kormaksson, Matthias, Vandemeulebroecke, Marc, Zhang, Cong, Martin, Ruvie, Gardiner, Stephen, Kwok, Chun Hei, West, Dominique M., Santos, Luis, Tian, Chengeng, Pang, Yu, Readie, Aimee, Ligozio, Gregory, Gandhi, Kunal K., Nichols, Thomas E., Mallon, Ann-Marie, Kelly, Luke, Ohlssen, David, and Nicholson, George

Published
2024
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7. Tutorial: Effective visual communication for the quantitative scientist

Author

Vandemeulebroecke, Marc, Baillie, Mark, Margolskee, Alison, and Magnusson, Baldur

Subjects
Statistics - Applications
Abstract

Effective visual communication is a core competency for pharmacometricians, statisticians, and more generally any quantitative scientist. It is essential in every step of a quantitative workflow, from scoping to execution and communicating results and conclusions. With this competency, we can better understand data and influence decisions towards appropriate actions. Without it, we can fool ourselves and others and pave the way to wrong conclusions and actions. The goal of this tutorial is to convey this competency. We posit three laws of effective visual communication for the quantitative scientist: have a clear purpose, show the data clearly, and make the message obvious. A concise "Cheat Sheet", available on https://graphicsprinciples.github.io, distills more granular recommendations for everyday practical use. Finally, these laws and recommendations are illustrated in four case studies.

Published
2019

8. Secukinumab in moderate-to-severe hidradenitis suppurativa (SUNSHINE and SUNRISE): week 16 and week 52 results of two identical, multicentre, randomised, placebo-controlled, double-blind phase 3 trials

Author

Kimball, Alexa B, Jemec, Gregor B E, Alavi, Afsaneh, Reguiai, Ziad, Gottlieb, Alice B, Bechara, Falk G, Paul, Carle, Giamarellos Bourboulis, Evangelos J, Villani, Axel P, Schwinn, Andreas, Ruëff, Franziska, Pillay Ramaya, Larisha, Reich, Adam, Lobo, Ines, Sinclair, Rodney, Passeron, Thierry, Martorell, Antonio, Mendes-Bastos, Pedro, Kokolakis, Georgios, Becherel, Pierre-Andre, Wozniak, Magdalena B, Martinez, Angela Llobet, Wei, Xiaoling, Uhlmann, Lorenz, Passera, Anna, Keefe, Deborah, Martin, Ruvie, Field, Clarice, Chen, Li, Vandemeulebroecke, Marc, Ravichandran, Shoba, and Muscianisi, Elisa

Published
2023
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12. Discussion on the meeting on ‘Data visualization’

Author

Diggle, Peter J., Baillie, Mark, Vandemeulebroecke, Marc, Jones, Wayne Robert, Fassò, Alessandro, Finazzi, Francesco, King, Thomas, Carter, Jackie, Noble, Alasdair, Krone, Tanja, Fang, Zhou, Green, Peter, Atkinson, Anthony C., Corbellini, Aldo, Morelli, Gianluca, Cressie, Noel, Friendly, Michael, Holmström, Lasse, Huser, Raphaël, de Carvalho, Miguel, Lombardo, Luigi, Jackson, Christopher, Mateu, Jorge, Reese, R. Allan, Wijayatunga, Priyantha, Zammit-Mangion, Andrew, Castruccio, Stefano, Genton, Marc G., Sun, Ying, Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., Gelman, A., and Bowman, Adrian W.

Published
2019

14. Response to anti-IL17 therapy in inflammatory disease is not strongly impacted by genetic background

Author

Zhang, Cong, primary, Shestopaloff, Konstantin, additional, Hollis, Benjamin, additional, Kwok, Chun Hei, additional, Hon, Claudia, additional, Hartmann, Nicole, additional, Tian, Chengeng, additional, Wozniak, Magdalena, additional, Santos, Luis, additional, West, Dominique, additional, Gardiner, Stephen, additional, Mallon, Ann-Marie, additional, Readie, Aimee, additional, Martin, Ruvie, additional, Nichols, Thomas, additional, Beste, Michael T., additional, Zierer, Jonas, additional, Ferrero, Enrico, additional, Vandemeulebroecke, Marc, additional, and Jostins-Dean, Luke, additional

Published
2023
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15. Assessing the validity and clinical meaningfulness of skin pain response (NRS30) assessed using numerical rating scale in hidradenitis suppurativa: Results from the SUNSHINE and SUNRISE trials

Author

Wei, Xiaoling, primary, Passera, Anna, additional, Muscianisi, Elisa, additional, Uhlmann, Lorenz, additional, Chen, Li, additional, Moreno, Santiago G., additional, Martin, Ruvie, additional, Vandemeulebroecke, Marc, additional, Keefe, Deborah, additional, Ravichandran, Shoba, additional, and Wozniak, Magdalena B., additional

Published
2023
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16. Coping with Information Loss and the Use of Auxiliary Sources of Data: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions

Author

Calderazzo, Silvia, primary, Tarima, Sergey, additional, Reid, Carissa, additional, Flournoy, Nancy, additional, Friede, Tim, additional, Geller, Nancy, additional, Rosenberger, James L, additional, Stallard, Nigel, additional, Ursino, Moreno, additional, Vandemeulebroecke, Marc, additional, Van Lancker, Kelly, additional, and Zohar, Sarah, additional

Published
2023
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17. Comparing algorithms for characterizing treatment effect heterogeneity in randomized trials.

Author

Sun, Sophie, Sechidis, Konstantinos, Chen, Yao, Lu, Jiarui, Ma, Chong, Mirshani, Ardalan, Ohlssen, David, Vandemeulebroecke, Marc, and Bornkamp, Björn

Abstract

The identification and estimation of heterogeneous treatment effects in biomedical clinical trials are challenging, because trials are typically planned to assess the treatment effect in the overall trial population. Nevertheless, the identification of how the treatment effect may vary across subgroups is of major importance for drug development. In this work, we review some existing simulation work and perform a simulation study to evaluate recent methods for identifying and estimating the heterogeneous treatments effects using various metrics and scenarios relevant for drug development. Our focus is not only on a comparison of the methods in general, but on how well these methods perform in simulation scenarios that reflect real clinical trials. We provide the R package benchtm that can be used to simulate synthetic biomarker distributions based on real clinical trial data and to create interpretable scenarios to benchmark methods for identification and estimation of treatment effect heterogeneity. [ABSTRACT FROM AUTHOR]

Published
2024
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21. Discussion on the paper by Hampson and Jennison

Author

Julious, Steven A., Dimairo, Munyaradzi, Stallard, Nigel, Glimm, Ekkehard, Bretz, Frank, Gallo, Paul, Minas, Giorgos, Aston, John A. D., Vandemeulebroecke, Marc, Posch, Martin, Gilmour, Steven, Bauer, Peter, Clarke, Bertrand, Bartroff, Jay, Burman, Carl-Fredrik, Coad, D. S., Draper, David, Mateu, Jorge, Rodríguez-Cortés, Francisco J., Roes, Kit C. B., Wong, Weng Kee, Sverdlov, Oleksandr, Lewis, Roger, Zini, Alessandro, Porcu, Emilio, and D'Ercole, Roberto

Published
2013
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27. Supplemental Material, sj-docx-1-tpx-10.1177_0192623320987202 - Biomarker-Based Classification and Localization of Renal Lesions Using Learned Representations of Histology—A Machine Learning Approach to Histopathology

Author

Freyre, Christophe A. C., Spiegel, Stephan, Keller, Caroline Gubser, Vandemeulebroecke, Marc, Hoefling, Holger, Dubost, Valerie, Cörek, Emre, Moulin, Pierre, and Hossain, Imtiaz

Subjects
FOS: Clinical medicine, 111599 Pharmacology and Pharmaceutical Sciences not elsewhere classified
Abstract

Supplemental Material, sj-docx-1-tpx-10.1177_0192623320987202 for Biomarker-Based Classification and Localization of Renal Lesions Using Learned Representations of Histology—A Machine Learning Approach to Histopathology by Christophe A. C. Freyre, Stephan Spiegel, Caroline Gubser Keller, Marc Vandemeulebroecke, Holger Hoefling, Valerie Dubost, Emre Cörek, Pierre Moulin and Imtiaz Hossain in Toxicologic Pathology

Published
2021
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28. Supplemental Material, sj-docx-2-tpx-10.1177_0192623320987202 - Biomarker-Based Classification and Localization of Renal Lesions Using Learned Representations of Histology—A Machine Learning Approach to Histopathology

Author

Freyre, Christophe A. C., Spiegel, Stephan, Keller, Caroline Gubser, Vandemeulebroecke, Marc, Hoefling, Holger, Dubost, Valerie, Cörek, Emre, Moulin, Pierre, and Hossain, Imtiaz

Subjects
FOS: Clinical medicine, 111599 Pharmacology and Pharmaceutical Sciences not elsewhere classified
Abstract

Supplemental Material, sj-docx-2-tpx-10.1177_0192623320987202 for Biomarker-Based Classification and Localization of Renal Lesions Using Learned Representations of Histology—A Machine Learning Approach to Histopathology by Christophe A. C. Freyre, Stephan Spiegel, Caroline Gubser Keller, Marc Vandemeulebroecke, Holger Hoefling, Valerie Dubost, Emre Cörek, Pierre Moulin and Imtiaz Hossain in Toxicologic Pathology

Published
2021
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33. Secukinumab, a human anti-IL-17A monoclonal antibody, for moderate to severe Crohnʼs disease: unexpected results of a randomised, double-blind placebo-controlled trial

Author

Hueber, Wolfgang, Sands, Bruce E, Lewitzky, Steve, Vandemeulebroecke, Marc, Reinisch, Walter, Higgins, Peter D R, Wehkamp, Jan, Feagan, Brian G, Yao, Michael D, Karczewski, Marek, Karczewski, Jacek, Pezous, Nicole, Bek, Stephan, Bruin, Gerard, Mellgard, Bjoern, Berger, Claudia, Londei, Marco, Bertolino, Arthur P, Tougas, Gervais, and Travis, Simon P L

Published
2012
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35. AFQ056 Treatment of Levodopa-Induced Dyskinesias: Results of 2 Randomized Controlled Trials

Author

Berg, Daniela, Godau, Jana, Trenkwalder, Claudia, Eggert, Karla, Csoti, IIona, Storch, Alexander, Huber, Heiko, Morelli-Canelo, Monica, Stamelou, Maria, Ries, Vincent, Wolz, Martin, Schneider, Christine, Di Paolo, Thérèse, Gasparini, Fabrizio, Hariry, Sam, Vandemeulebroecke, Marc, Abi-Saab, Walid, Cooke, Katy, Johns, Donald, and Gomez-Mancilla, Baltazar

Published
2011
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36. A Longitudinal Item Response Theory Model to Characterize Cognition Over Time in Elderly Subjects

Author

Vandemeulebroecke, Marc, Bornkamp, Björn, Krahnke, Tillmann, Mielke, Johanna, Monsch, Andreas, and Quarg, Peter

Subjects
Aged, 80 and over, Male, Genotype, Apolipoprotein E4, Original Articles, Middle Aged, Neuropsychological Tests, Models, Biological, Cognition, Educational Status, Humans, Learning, Original Article, Female, Aged
Abstract

For drug development in neurodegenerative diseases such as Alzheimer's disease, it is important to understand which cognitive domains carry the most information on the earliest signs of cognitive decline, and which subject characteristics are associated with a faster decline. A longitudinal Item Response Theory (IRT) model was developed for the Basel Study on the Elderly, in which the Consortium to Establish a Registry for Alzheimer's Disease - Neuropsychological Assessment Battery (with additions) and the California Verbal Learning Test were measured on 1,750 elderly subjects for up to 13.9 years. The model jointly captured the multifaceted nature of cognition and its longitudinal trajectory. The word list learning and delayed recall tasks carried the most information. Greater age at baseline, fewer years of education, and positive APOEɛ4 carrier status were associated with a faster cognitive decline. Longitudinal IRT modeling is a powerful approach for progressive diseases with multifaceted endpoints.

Published
2017

39. Discussion on the meeting on 'Data visualization'

Author

Wijayatunga, Priyantha, Diggle, Peter J., Baillie, Mark, Vandemeulebroecke, Marc, Jones, Wayne Robert, Fassò, Alessandro, Finazzi, Francesco, King, Thomas, Carter, Jackie, Noble, Alasdair, Krone, Tanja, Fang, Zhou, Green, Peter, Atkinson, Anthony C., Corbellini, Aldo, Morelli, Gianluca, Cressie, Noel, Friendly, Michael, Holmström, Lasse, Huser, Raphaël, de Carvalho, Miguel, Lombardo, Luigi, Jackson, Christopher, Mateu, Jorge, Reese, Allan, Zammit-Mangion, Andrew, Castruccio, Stefano, Genton, Marc G., Sun, Ying, Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., Gelman, A., Bowman, Adrian W., Wijayatunga, Priyantha, Diggle, Peter J., Baillie, Mark, Vandemeulebroecke, Marc, Jones, Wayne Robert, Fassò, Alessandro, Finazzi, Francesco, King, Thomas, Carter, Jackie, Noble, Alasdair, Krone, Tanja, Fang, Zhou, Green, Peter, Atkinson, Anthony C., Corbellini, Aldo, Morelli, Gianluca, Cressie, Noel, Friendly, Michael, Holmström, Lasse, Huser, Raphaël, de Carvalho, Miguel, Lombardo, Luigi, Jackson, Christopher, Mateu, Jorge, Reese, Allan, Zammit-Mangion, Andrew, Castruccio, Stefano, Genton, Marc G., Sun, Ying, Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., Gelman, A., and Bowman, Adrian W.

Abstract

Visualizing both conditional and marginal associations in contingency tables by using simple diagrams is discussed

Published
2019
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46. Multistate modeling and simulation of patient trajectories after allogeneic hematopoietic stem cell transplantation to inform drug development.

Author

James, David A., Ng, Jennifer, Wei, Jiawei, and Vandemeulebroecke, Marc

Abstract

We present a case study for developing clinical trial scenarios in a complex progressive disease with multiple events of interest. The idea is to first capture the course of the disease in a multistate Markov model, and then to simulate clinical trials from this model, including a variety of hypothesized drug effects. This case study focuses on the prevention of graft‐versus‐host disease (GvHD) after allogeneic hematopoietic stem cell transplantation (HSCT). The patient trajectory after HSCT is characterized by a complex interplay of various events of interest, and there is no established best method of measuring and/or analyzing treatment benefits. We characterized patient trajectories by means of multistate models that we fitted to a subset of the Center for International Blood and Marrow Transplant Research (CIBMTR) database. Events of interest included acute GvHD of grade III or IV, severe chronic GvHD, relapse of the underlying disease, and death. The transition probability matrix was estimated using the Aalen‐Johansen estimator, and patient characteristics were identified that were associated with different transition rates. In a second step, clinical trial scenarios were simulated from the model assuming various drug effects on the background transition rates, and the operating characteristics of different endpoints and analysis strategies were compared in these scenarios. This helped devise a drug development strategy in GvHD prevention after allogeneic HSCT. More generally, multistate models provide a rich framework for exploring complex progressive diseases, and the availability of a corresponding simulation machinery provides great flexibility for clinical trial planning. [ABSTRACT FROM AUTHOR]

Published
2019
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47. How can we make better graphs? An initiative to increase the graphical expertise and productivity of quantitative scientists.

Author

Vandemeulebroecke, Marc, Baillie, Mark, Carr, David, Kanitra, Linda, Margolskee, Alison, Wright, Andrew, and Magnusson, Baldur

Subjects
*FINGOLIMOD, *GRAPHIC methods, *EXPERTISE
Abstract

Graphics are at the core of exploring and understanding data, communicating results and conclusions, and supporting decision‐making. Increasing our graphical expertise can significantly strengthen our impact as professional statisticians and quantitative scientists. In this article, we present a concerted effort to improve the way we create graphics at Novartis. We provide our vision and guiding principles, before describing seven work packages in more detail. The actions, principles, and experiences laid out in this paper are applicable generally, also beyond drug development, which is our field of work. The purpose of this article is to share our experiences and help foster the use of good graphs in pharmaceutical statistics and beyond. A Graphics Principles "Cheat Sheet" is available online at https://graphicsprinciples.github.io/. [ABSTRACT FROM AUTHOR]

Published
2019
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50. Multivariate Analysis of Longitudinal Ordinal Data with Mixed E ects Models, with Application to Clinical Outcomes in Osteoarthritis

Author

Laffont, Celine Marielle, Vandemeulebroecke, Marc, Concordet, Didier, ToxAlim (ToxAlim), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), and Novartis Pharma AG

Subjects
[STAT.AP]Statistics [stat]/Applications [stat.AP], pairwise fi tting, categorical data, pseudolikelihood, [STAT.ME]Statistics [stat]/Methodology [stat.ME], clinical scores
Abstract

Our objective was to evaluate the efficacy of robenacoxib in osteoarthritic dogs using four ordinal responses measured repeatedly over time. We propose a multivariate probit mixed effects model to describe the joint evolution of endpoints and to evidence the intrinsic correlations between responses that are not due to treatment effect. Maximum likelihood computation is intractable within reasonable time frames. We therefore use a pairwise modeling approach in combination with a stochastic EM algorithm. Multidimensional ordinal responses with longitudinal measurements are a common feature in clinical trials. However, the standard methods for data analysis use unidimensional models, resulting in a loss of information. Our methodology provides substantially greater insight than these methods for the evaluation of treatment effects and shows a good performance at low computational cost. We thus believe that it could be used in routine practice to optimize the evaluation of treatment efficacy.

Published
2013

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