CALL FOR SPEAKERS STILL OPEN
Abstract Submission deadline: May 10th, 2020
Abstract Submission deadline: May 10th, 2020
Data Science and the Rise of New Analytical Techniques
The Evolution of the Clinical Development Paradigm and Biostatistics
- 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
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.
Jens-Otto Andreas - Head Statistical Sciences & Innovation - Bone & New Diseases at UCB Biosciences GmbH
Lisa Comarella - Director Biostatistics at CROS NT
Giacomo Mordenti - Director, Statistics & Data Management at Livanova
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
- Academic Research
For sponsorship opportunities, please contact firstname.lastname@example.org
9 November 2020
A full day training on Data Science and Machine Learning Methodology
Stay tuned for further details!
10 Novembre 2020
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.
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.
Breiman, L (2001) Statistical Modelling: The Two Cultures. Statistical Science, Vol 16, No.3, 199-231.
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?
Sede della Conferenza
NH Danube City
Wagramer Strasse 21
The NH Danube City hotel is ideally located for business and pleasure. Not only is it steps from the UN building and the Vienna International Center, but it also puts you in easy reach of the recreational activities on Danube Island. Vienna’s biggest shopping center, the Donauzentrum, is also on the doorstep. 10 minutes to the city center from the nearby subway station. Awarded the ISO 14001 environmental certificate, thanks to its environmental commitment.
From the Airport
Train: Take the CAT to Landstraße - Wien Mitte, then change onto the U4 (green line) towards Heiligenstadt. Change at Schwedenplat station onto the U1 (red line) towards Leopoldau. Get off the metro at the Kaisermühlen-Vienna International Center station. Take the Wagramer Straße exit and walk around 500 m away from the city center to reach the hotel.
S-Bahn: Take the S7 to the Praterstern station. Change onto the U1 (red line) towards Leopoldayu. Get off the metro at the Kaisermühlen-Vienna International Center station. Take the Wagramer Straße exit and walk around 500 m away from the city center to reach the hotel.
Bus: Vienna Airport Lines: Vienna Airport Lines buses stop directly opposite the arrivals halls exit and will drop you off at the stop right in front of the hotel.
Taxis: It is a 25 minutes trip, depending on traffic and cost around 40€ - 45€
From the Train Station:
From Kaisermühlen-Vienna International Center: take the Exit to the "Wagramer Straße", turn left and walk straight for around 5 minutes. The hotel is located on the left.
Closest metro station: Kaisermühlen-Vienna International Center (U1)
You can choose between a single ticket (2,80€), a day ticket (7,60€) or a ticket for a week (16,20€). Within the ticket price, you can take the train and the bus in the inner circle of Vienna.
The hotel's GPS Coordinates: 48.235364°N 16.422006°E - Parking: Onsite: 2,60€/hour, 33,80€/day.
Suggested hotels in the nearby:
- Park Inn by Radisson Uno City Vienna (at 190 mt)
- Arcotel Kaiserwasser Superior (at 550 mt)
At your arrival you will find our Staff at the Welcome Desk to greet and make you feel welcome and as comfortable as possible, providing direction and all information about the meeting, seating, refreshments.
Coffee breaks and networking lunch will provide a pleasant moment of refreshment and an opportunity for you to network with your industry peers. Special care about the food quality, in particular to your dietary needs or preferences, from menus of local products and flavors to vegetarian dishes.
All our conference facilities are safe, healthy, comfortable, aesthetically-pleasing, and accessible. They are able to accommodate the specific space and equipment needs of the conference. Equipped for audio/visual, Free Wi-Fi to allow everyone to connect with just one click.
All the necessary information for the Conference is provided. The environment is important to us and that's why we try to make this meeting an experience of environmental sustainability.
Seat at the conference, copy of presentations of Speakers who allow the distribution, informative literature for the day, networking lunch, coffee break, organisational office assistance, certificate of attendance.