Date

9 - 10 November 2020

Location

Virtual

Language

English

European Statistical Forum Virtual

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European Statistical Forum Virtual

All times indicated on the Agenda are Central Europe Summer Time.

Data Science and the Rise of New Analytical Techniques

The Evolution of the Clinical Development Paradigm and Biostatistics


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


For sponsorship opportunities, please contact events@lsacademy.com

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9 November 2020   |  2:00 PM - 6:00 PM CEST

PRE-CONFERENCE VIRTUAL 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

Course language: English

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

REGISTRATIONS FEES PRE-CONFERENCE VIRTUAL SEMINAR

Early Bird: € 570,00* (until 9 October 2020)

Ordinary: € 665,00*

Freelance - Academy - Public Administration**: € 366,00*

* for Italian companies: +22% VAT
**Early Bird discount not applicable to Freelance – Academy – Public Administration fee

If you would like to attend both, pre-conference virtual seminar and virtual conference, please take a look at tickets prices

9 November 2020

The programme will cover:

  • 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

Markus  Lange Markus Lange – Senior Principal Statistical Consultant at Novartis AG
Lorenz  Uhlmann Lorenz Uhlmann – Principal Biostatistician at Novartis AG

The programme will cover:

  • 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

Markus  Lange Markus Lange – Senior Principal Statistical Consultant at Novartis AG
Lorenz  Uhlmann Lorenz Uhlmann – Principal Biostatistician at Novartis AG

10 November 2020

In 2001, Leo Breiman, a Professor in the Department of Statistics, University of California, Berkeley, wrote an influential – and not uncontroversial – paper, in which he stated: “There are two cultures in the use of statistical modelling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown.” The “data modelling” culture (so-named by Professor Breiman) obsesses with identifying a specific form of nature lying inside the black box that turns inputs (of whatever form) into outputs (response values of scientific interest) by specifying parametric data generating mechanisms, fitting these to available data, then making predictions and/or classifications about the output variables conditional on that fit. Data modellers are nearly always what we’d call “statisticians”. 

Breiman set this up in opposition to what he called the “algorithmic modelling” culture, which (supposedly) doesn’t care about the structure within the black box, and instead identifies a function that links input with output with sufficiently high accuracy. Such an objective is indeed how the ML scientists of my acquaintance define their work. 

But is the disjunction (data vs algorithm) valid? I will argue that it isn’t – that any successful modelling activity requires an understanding of the properties of both data and algorithm, and whether one calls oneself a statistician or an ML scientist is a matter to which the universe, with regard to the validity of one’s predictions, is entirely indifferent. 

The real cultural separation, I will argue, between the statistical and ML communities (and why the former keeps losing talent to the latter) isn’t to do with models for data and algorithms, but is much less tangible, and to do with disposition: “you shouldn’t do that”, versus “why not try?” This is the real challenge to the statistical community: can we open-up our attitudes quickly enough to remain relevant in a world that ought to be crying out for our talents. We seem to have boxed ourselves in to something unfashionable; I think we can learn from our ML friends in order to change, and the best way to learn is to work together. I’ll explore all these thoughts in the talk with some examples from my work in pharmaceutical R&D.


Reference
Breiman, L (2001) Statistical Modelling: The Two Cultures. Statistical Science, Vol 16, No.3, 199-231.

Graeme Archer Graeme Archer – VP & Head, Non-Clinical & Translational Statistics at GlaxoSmithKline Pharmaceuticals R&D

The National Institute for Health and Care Excellence (NICE) provides national guidance and advice to improve health and social care. In January 2020 we published a Statement of Intent to increase and extend the use of data in the development and evaluation of our guidance. This explores:

  • What kind of evidence does NICE currently use to develop guidance
  • What broader types of data are available
  • When and why should broader types of data be considered
  • Practical considerations associated with data analytics

We have since defined the programme to develop a methods and standards framework for activities involving broader sources of data and applied analytics. This is summarised into five key topics, which are crucial to address to ensure best practice in conducting high quality analyses of data. These are Research Governance; Data; Analysis; Results and Dissemination. In addition to these topics, the programme will consider some cross cutting issues ranging from transparency and public trust, to the validation and evaluation of artificial intelligence [AI] in digital technology.

The presentation will give an overview of the transformational journey and the detail on the issues we are considering; which will be crucial for any organisation seeking to ensure their approach is suitable for regulatory assessment/evaluation.

Thomas Lawrence Thomas Lawrence – Data scientist, Managed Access at NICE

In the 21st century, data is the most valuable asset of companies and researchers. For many fields, the availability of Big Data holds the promise to answer questions that would have been out of reach just a couple of years ago. The analysis of Big Data requires expert knowledge, which is why data science is becoming an increasingly important topic. There is an enormous curiosity to explore new approaches and an increased visibility of data analytics to support decision making.  However, the focus is often on data analysis and important steps of design, report, and communicating complex concepts are not addressed. Statistics can make a decisive contribution to a successful and safe application of e.g. machine learning (ML) techniques. While there are workshops on data science available, teaching the relevant techniques with real-life application to medical data is currently still a niche subject. To fill this gap, the Department of Medical Biometry (University of Heidelberg) offers a certified course that introduces and deepens the essentials of medical data science. The course is structured into four different modules teaching the skills to answer clinically relevant questions by analyzing Big Data. This talk provides insight into the programme and addresses the challenges and opportunities of applying ML methods in biostatistics by the presentation of a practical example on prediction dealing with clustered or nested data. Clustered data is mainly analyzed using generalized mixed-effects regression models, because ML algorithms, like tree-based models, usually do not consider clustered data structure. Fokkema et al. (2018) propose the generalized linear mixed-effects model tree (GLMM tree) algorithm which accounts for the clustered data structure and automatically performs variable selection as done by classical tree-based algorithms. The GLMM tree algorithm will be illustrated in the context of developing a prediction tool for tooth loss, where teeth are clustered within patients. To conclude, ML techniques can facilitate workflows, but there are situations where these methods may need further development to be applicable to the particular needs of medical research. Teaching the relevant techniques for a safe application is important.  

References: M. Fokkema, N. Smits, A. Zeileis, et al. (2018). Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees. Behav Res, 50:2016–2034. 

Marietta Kirchner Marietta Kirchner – Biostatistician at Institute of Medical Biometry and Biostatistics, University of Heidelberg

Biotech and Pharmaceutical companies alike are increasingly relying on Artificial Intelligence applied on expertly curated data to identify complex patterns in order to better understand and minimize the risk of drug development. 

Example use case: 

What is the probability of technical and regulatory success (PTRS) for a program in clinical development and what are key drivers for this? The application of Machine Learning techniques can help analyze external historic data and provide a powerful perspective. We examine as a case study an ongoing program in Diffuse Large B-Cell Lymphoma (DLBCL), and address several related questions, e.g., (a) how does the PTRS in this program compare to historic averages, (b) how does the PTRS compare to competing ongoing programs, (c) what are the key drivers (e.g., outcomes, number of patients) for the Machine Learning analysis, (d) what are historic benchmarks for such drivers, (e) how do historic benchmarks compare to current trends in ongoing clinical development programs?

Dimitrios Skaltsas Dimitrios Skaltsas – Co-founder & Executive Director at Intelligencia

Johann De Jong (UCB); Ioana Cutcutache (UCB); Sami Elmoufti (UCB); Robert Power (UCB); Cynthia Dilley (UCB); Matthew Page (UCB); Martin Armstrong (UCB); Holger Froehlich (former UCB 1)

Epilepsy treatment and drug development are hampered by high rates of non-response. A better understanding of non-response can help clinical trial design and disease management, by focusing therapies on probable responders, and providing optimal treatment earlier in a patient’s disease course.

We collected clinical trial data for the anti-epileptic drug (AED) brivaracetam (n = 235 patients; single trial). We combined a hybrid data-/knowledge-driven feature extraction with advanced machine learning to systematically integrate available clinical and genetic data, and successfully predict drug response (cross-validated AUC = 0.76, external validation cohort: AUC = 0.75). The most important predictors represented a mix of clinical and genetic features, e.g. prior AED use, epileptic focus localization and enrichment of certain classes of genetic variants. Additionally, we showed that by enriching for responders, such models can be used to substantially reduce sample sizes required in confirmatory studies, albeit at the expense of stricter inclusion-/exclusion criteria.

Our analysis represents the first of its kind in epilepsy: Even with limited sample size, integrating clinical and genetic data can inform AED response prediction. Furthermore, such models can substantially impact clinical trial design. This also shows that we can begin thinking more systematically about personalized healthcare in epilepsy.

UCB Pharma-funded


1. Current affiliation: Fraunhofer Institute for Scientific Computing and Algorithmcs (SCAI), Business Area Bioinformatics, Schloss Birlinghoven, 53754 Sankt Augustin, Germany
Email: holger.froehlich@scai.fraunhofer.de

Johann  de Jong Johann de Jong – Principal Scientist / A.I. Data Scientist at UCB

In recent years there has been an massive increase in the use of advanced analytics techniques in a wide variety of areas associated with clinical development. This presentation will provide details of a number of such examples such as finding drug candidates to include in clinical trials, site selection, subgroup detection and analysis of sensor data. Reflection on what has worked well and where opportunities have not been fully grasped will be given and suggestions for new areas in clinical development where data science could be applied will be provided. 

David Wright David Wright – Head of Statistical Innovation at AstraZeneca

Speakers' Panel:

Graeme Archer - VP & Head, Non-Clinical & Translational Statistics at GlaxoSmithKline Pharmaceuticals R&D
Johann de Jong - Principal Scientist / A.I. Data Scientist at UCB
Marietta Kirchner - Biostatistician at Institute of Medical Biometry and Biostatistics, University of Heidelberg
Thomas Lawrence - Data scientist, Managed Access at NICE
Dimitrios Skaltsas - Co-founder & Executive Director at Intelligencia
David Wright - Head of Statistical Innovation at AstraZeneca

Scientific Board

Jens-Otto Andreas
Jens-Otto Andreas Project Lead Statistician at UCB Biosciences GmbH
Lisa Comarella
Lisa Comarella Director Biostatistics at CROS NT
Giacomo Mordenti
Giacomo Mordenti Head of Biostatistics & Data Management Europe at Daiichi Sankyo Europe GmbH
Marc Vandemeulebroecke
Marc Vandemeulebroecke Global Group Head for Dermatology at Novartis Biostatistics

Speakers

Graeme Archer
Graeme Archer VP & Head, Non-Clinical & Translational Statistics at GlaxoSmithKline Pharmaceuticals R&D
Johann  de Jong
Johann de Jong Principal Scientist / A.I. Data Scientist at UCB
Marietta Kirchner
Marietta Kirchner Biostatistician at Institute of Medical Biometry and Biostatistics, University of Heidelberg
Markus  Lange
Markus Lange Senior Principal Statistical Consultant at Novartis AG
Thomas Lawrence
Thomas Lawrence Data scientist, Managed Access at NICE
Dimitrios Skaltsas
Dimitrios Skaltsas Co-founder & Executive Director at Intelligencia
Lorenz  Uhlmann
Lorenz Uhlmann Principal Biostatistician at Novartis AG
David Wright
David Wright Head of Statistical Innovation at AstraZeneca

Scientific Board

Jens-Otto Andreas
Jens-Otto Andreas Project Lead Statistician at UCB Biosciences GmbH
Lisa Comarella
Lisa Comarella Director Biostatistics at CROS NT
Giacomo Mordenti
Giacomo Mordenti Head of Biostatistics & Data Management Europe at Daiichi Sankyo Europe GmbH
Marc Vandemeulebroecke
Marc Vandemeulebroecke Global Group Head for Dermatology at Novartis Biostatistics

Conference Venue

European Statistical Forum Virtual

Virtual conference with presentations, slots for Q&A and discussion among delegates.

Useful information

LS Academy will provide the link to join the conference some days before the conference date.

All times indicated on the Agenda are Central Europe Summer Time.

Fee includes: access to the virtual conference, organizational support, certificate of attendance, slide presentations in pdf format provided post-event.

Useful numbers

Ilaria Butta
Ilaria Butta Events & Training Executive
Enrico Pedroni
Enrico Pedroni Managing Director
Valeria Quintily
Valeria Quintily Project & Scientific Manager

Past Events

Conference Price

690.00 €

Early Bird

Friday, October 9, 2020

590.00 €

Payments accepted

  • Bank Transfer
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  • PayPal

For information:

Contacts:

Ilaria Butta
Ilaria Butta Events & Training Executive
Enrico Pedroni
Enrico Pedroni Managing Director
Valeria Quintily
Valeria Quintily Project & Scientific Manager
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The course will proceed with a minimum of 8 participants. Should this number not be reached the registered participants will be notified one week prior to the commencement of the course.
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