Introduzione
The application of big data analytics like machine learning and artificial intelligence holds the potential to transform drug development by means of improved patient outcomes, identification of new treatments and reduction of costs and development time.
This transformation is facilitated not only by the development of the analytical techniques, but also by the ever-expanding availability of data from legacy information, clinical trials, and medical records from participants in data collection initiatives as well as the even broader “real world”.
The enrichment of biological, clinical and patient preference large-scale data could enable computational inference relevant to real-world pharmaceutical research, particularly in the areas of:
- Identification of predictive factors for patient response
- More efficient clinical trial management and recruitment
- Supplementing clinical trial data with real world data
- Drug candidate selection and pipeline development
- Orphan drugs, rare diseases and drug repurposing
The 11th edition of the European Statistical Forum is therefore dedicated to understanding if and how these new techniques may be changing the drug development paradigm, to highlighting opportunities and pitfalls, and to exploring how the role of the biostatistician may evolve and interact with these new approaches.
In scope are presentations for example on:
- Regulatory views on the new analytical techniques incorporating data science elements
- Advanced methods for response prediction or subgroup identification
- Novel approaches to trial design incorporating flexible elements for patient stratification
- Innovative ways to incorporate real world data into the clinical study design or analysis
- Early identification of Adverse Drug Reactions
- Real case studies of collaboration between biostatisticians and data scientists
- Opportunities and risks with novel data science methodologies.
The European Statistical Forum conference will be preceded by a seminar / training on data science and machine learning methodology.
Scientific board
Jens-Otto Andreas - Head Statistical Sciences & Innovation - Bone & New Diseases at UCB Biosciences GmbH
Lisa Comarella - Director Biostatistics at CROS NT
Giacomo Mordenti - Head of Biostatistics & Data Management Europe at Daiichi Sankyo Europe GmbH
Marc Vandemeulebroecke - Global Group Head for Dermatology at Novartis Biostatistics
Who should attend?
The conference is addressed to statisticians, pharmacometricians, physicians, regulators, academia and other experts interested in the field belonging to: Pharmaceutical, and Biotechnology companies, CROs, Universities/Hospitals, Academic Research.
Programma
All times indicated are Central Europe Time
Relatori
Jens-Otto Andreas
Jens-Otto received a diploma in Mathematics in 1993. In 1994 he started his career in Biostatistics at Grünenthal GmbH in Aachen. Here he worked in several therapeutical areas like gynecology and pain. Later on he became a specialist in the Phase 1 area. In 2005 he started to UCB (legacy Schwarz Pharma) as a Project Biostatistician in Phase 1. With the restructuring at UCB in 2008 Jens-Otto became the Head EU Biostatistics supervising Biostatisticians located in Monheim (Germany) and Brussels. Since 2016 he is also the Head of the East Asia Biostatistics of UCB. UCB’s key indications are CNS and immunology. From 2017 to 2019 Jens-Otto was the Head of Statistical Sciences – Bone & New Diseases at UCB Biosciences GmbH. Currently Jens-Otto is holding the position of a SSI Business Operations Excellence Senior Lead at UCB Biosciences GmbH.
Lisa Comarella
Lisa has over 20 years of experience in the clinical research industry. In her current role, Lisa is responsible for the management of the biostatistics team including the development professional growth, supervision of the quality of deliverables and outputs, as well as the management of processes for biostatistical activities to ensure they are up-to-date and aligned with the business need and regulatory requirements. Lisa’s areas of expertise include DSMB support, submission studies, integrated summaries, writing Statistical Analysis Plans and contributing to Clinical Study Reports. She has worked in a variety of therapeutic areas and has particular expertise in respiratory, cardiovascular, infectious diseases and oncology. She is a committee member of ESF (European Statistical Forum) and is also a member of several associations including PSI (Statisticians in the Pharmaceutical Industry) and EFSPI (European Federation of Statisticians of the Pharmaceutical Industry). Lisa has been a contributing author on scientific articles in cardiology, diabetes and oncology.
Giacomo Mordenti
After the degree in statistical sciences from the University of Florence (1998), Giacomo stared his career in drug development in Menarini where he was exposed to different therapeutic areas and development phases. In 2007 he moved to Geneva to join Merck Serono, where he led a biostatistical team dedicated to early development in oncology. In 2013 Giacomo joined Grunenthal as global head of Biostatistics. In 2016 he moved to medical device industry in Livanova as Global Head of Data Management and Statistics. In 2020 he joined Daiichi-Sankyo as Head of Biostatistcs and Data Management Europe. Since 2011 he is part of the scientific board of European Statistical Forum; since 2013 he is part of the European Statistical Leader group of EFSPI. His main research interests are in the field of Bayesian statistics, adaptive design and applications of data science techniques to drug development.
Marc Vandemeulebroecke
Marc Vandemeulebroecke joined Novartis in 2006, coming from Schering AG in Berlin. He has been supporting development programs in early and late phase development across various disease areas (incl. Neuroscience, Gastrointestinal, Parasitology, Cardio-metabolic, Immunology, Transplant and Hepatology) as statistician and pharmacometrician. Currently he is Global Group Head for Dermatology. Marc holds a maîtrise in mathematics from the University Paris XI, a diploma in mathematics from the University of Münster, a PhD in mathematical statistics from the University of Magdeburg, and an MSc in PKPD modeling from the University of Manchester. He received the Gustav-Adolf-Lienert award from the German Region of the International Biometric Society (IBS) for his PhD thesis, which focused on adaptive designs. He co-authored various scientific publications and one R package and is Associate Editor of Pharmaceutical Statistics. Marc’s current interests include statistical graphics and machine learning.
Graeme Archer
Graeme Archer is VP & Head of the Non-clinical and Translational Statistics (NCTS) group. NCTS ensures integrity in the design, analysis and interpretation of models and experiments developed by GSK R&D’s Research Units – Graeme is particularly focused on ‘translation’ (what do we predict will be observed in man, and how confident in those predictions should we be?) On GSK’s Research governance boards he strives for statistical rigour in portfolio decision-making – and was joint winner of the 2019 Royal Statistical Society’s Pharmaceutical Excellence award for his work on Quantitative Decision-Making. His 1990s PhD, from the University of Glasgow, concerned Bayesian methods for image analysis – early ‘machine learning’? – and as a consequence he is a champion for synergy between the statistical and AI/ML communities. Graeme spent a sabbatical year in 2016 as a speech-writer for a British cabinet minister.
Johann de Jong
Johann de Jong obtained his PhD in Computational Cancer Biology from Delft University of Technology. Since then, he has gained experience in a wide variety of domains, ranging from gene regulation and chromatin biology to cancer research and neurological disorders, in both academia (the Netherlands Cancer Institute) and industry (BASF, UCB). He currently works as a Principal Scientist at UCB, and develops and applies methods grounded in machine learning (including deep learning) and statistics for the analysis of biological and clinical datasets, within the context of precision medicine.
Marietta Kirchner
I studied sports medicine and maths in Frankfurt am Main (Germany) and received my PhD in 2013 working on nonlinear time series analysis. In July 2013 I successfully applied for a position at the Department of Medical Biometry in Heidelberg. As biostatistician I am involved in clinical trials. Additionally, I am responsible for our Master’s programme Medical Biometry/ Biostatistics and set up our certificate programme Medical Data Science in 2018/19.
Markus Lange
- Studies of mathematics at the Ruhr-University Bochum
- Doctoral thesis at the Hannover medical school
- More than 5 years of industry experience
- Senior Principal Statistical Consultant at Novartis
Thomas Lawrence
Tom is a data scientist in the data and analytics team at the UK National Institute for Health and Care Excellence (NICE). This team provides central oversight, advice and support on all aspects of data and analytics; spearheading the transformation programme to enable more sophisticated collection, management, storage and exploitation of data across NICE to enable better evidence based decisions across all its business.
Tom joined NICE in 2019 following 10 years of working in data science at the University of Manchester.
Dimitrios Skaltsas
Dimitrios is co-founder and Executive Director to Intelligencia, a NY based data science company. Intelligencia focuses on making novel therapies available to patients faster, by assessing their probability of success during clinical development. Prior to that, Dimitris was with McKinsey & Co, as a consultant and later as the domain lead for New Ventures in Life Sciences R&D. He has worked with organizations and governments in North America, Europe, Middle East, Africa and SE Asia. He is an advisor and angel investor to technology companies with a focus on big data. Dimitris holds an LLM from UCL, and an MBA from INSEAD, where he currently serves as an Executive in Residence for AI/ Digital.
Lorenz Uhlmann
- Studies of statistics at the LMU Munich
- Doctoral thesis at the Institute for Medical Biometry and Informatics (IMBI), Heidelberg University
- Head of the working group “Statistical Modeling” at the IMBI
- Principal Biostatistician at Novartis
Seminar
9 November 2020 | 2:00 PM – 6:00 PM CET
PRE-CONFERENCE SEMINAR
Machine Learning in clinical drug development
Advanced statistical tools and techniques
Introduction
There is tremendous interest and excitement surrounding the application of Machine learning (ML) in drug development. Machine Learning (ML) tools can process information much faster, cheaper and more accurately than any human, and some people expect no less than a change to the clinical drug development paradigm.
In this online course, we will get the participants up to speed with the opportunities of ML in drug development. We will discuss the statistical details behind the ideas, the implementation using software (R) as well as the interpretation of the results. Any examples will be inspired by real problems.
Who should attend?
Recommended for any quantitative scientist seeking an overview of machine learning and artificial intelligence (AI) and its application in the pharmaceutical industry.
Programme
Overview of ML in pharma – “match made in heaven” or “it’s complicated”?
Discussion of key ML concepts
Key elements and principles for building and assessing supervised machine learning methods (e.g. loss functions, metrics, cross-validation, hold out data, bootstrap)
(Regularized) regression models such as Lasso, Ridge, Elastic Net, GAM
Ensemble methods based on classification and regression trees (e.g bagging, random forest and boosting)
A basic knowledge of Neural Networks and how they lead to deep learning methods
Type of training
Shared presentation by Markus und Lorenz that aims to provide theoretical background and practical examples. Questions are welcome, we are hoping for lively discussions.
Lecturers
Markus Lange, Senior Principal Statistical Consultant – Novartis AG
- Studies of mathematics at the Ruhr-University Bochum
- Doctoral thesis at the Hannover medical school
- More than 5 years of industry experience
- Senior Principal Statistical Consultant at Novartis
Lorenz Uhlmann, Principal Biostatistician – Novartis AG
- Studies of statistics at the LMU Munich
- Doctoral thesis at the Institute for Medical Biometry and Informatics (IMBI), Heidelberg University
- Head of the working group “Statistical Modeling” at the IMBI
- Principal Biostatistician at Novartis
Participant experience
The attendees should have solid knowledge of general statistics (such as generalized linear models). Basic knowledge of R programming is recommended but not required.
At the end of the training, you will be able to:
- understand different types of machine learning (e.g. supervised, unsupervised, re-enforcement) and the types of problems where they might be applied
- identify whether it is appropriate to apply machine learning or artificial intelligence techniques to a drug development problem
- assess and provide guidance on ML and AI solutions proposed by others (e.g. external vendors)
- interpret results from machine learning solutions
- get started if you want to apply the discussed techniques on your own
Contatti
Ilaria Butta
Events & Training Executive
+39 379.1492960
ilaria.butta@lsacademy.com
Valeria Quintily
Project & Scientific Manager
+39 329.4683329
valeria.quintily@lsacademy.com
Quote di iscrizione
CONFERENCE
€ 590,00* Early Bird fee until October 30th, 2020
€ 690,00* Ordinary fee
€ 430,00* Freelance, Academy, Public AdministrationPRE-CONFERENCE SEMINAR
€ 570,00* Early Bird fee until October 30th, 2020
€ 665,00* Ordinary fee
€ 360,00* Freelance, Academy, Public AdministrationPRE-CONFERENCE SEMINAR + CONFERENCE
€ 1080,00* Early Bird fee until October 30th, 2020
€ 1195,00* Ordinary fee
€ 670,00* Freelance, Academy, Public Administration
* for Italian companies: +22% VAT
Fee includes: access to the virtual seminar/conference, organizational support, certificate of attendance, slide presentations in pdf format provided post-event.
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