Abstract submission deadline: 31 March 2023
Submit your speaking proposal

Precision medicine, so called personalized medicine, is “an approach to tailoring disease prevention and treatment that takes into account differences in people’s genes, environments and lifestyle”, according to FDA definition.

Innovative clinical trial designs, for example, platform trials, basket/umbrella trials are developed to support the development of precision medicine. Master protocol, one overarching protocol that allows simultaneously evaluation of multiple therapies or/and multiple diseases, has made these innovative clinical trial designs more implementable and is becoming to supplant classic trials, phase I, II, and III protocols in some therapeutical areas (e.g., oncology).
In addition, precision medicine revolves around a multitude of different data sources and types (e.g., ‘omics’ data, real world data (RWD), which ask for more advanced statistical methodologies.

This opens the door for the application of Artificial intelligence (AI)/Machine learning (ML) to unlock the potential of these data and advance precision medicine.

The 14th edition of European Statistical Forum (ESForum) aims to inform on current status of methodology and regulatory aspects and to discuss the future direction of clinical trial design in the era of precision medicine and to debate the role of AI/ML and RWD in the rapidly changing landscape of clinical trial designs.

This will include presentations focusing on:

  • Current landscape of clinical trial designs in the era of precision medicine
  • Regulatory views on methodology used in precision medicine
  • Recent development of AI/ML in advance precision medicine development
  • The role of RWD to fuel precision medicine
  • Case studies and practical approaches

Scientific Board
Jens-Otto Andreas - SSI Business Operational Excellence Senior Lead at UCB Biosciences
Lisa Comarella - Senior Director Biostatistics at Alira Health
Marco Eigenmann - Principal Biostatistician at Novartis
Victoria Strauss - Senior Principal Therapeutic Area Methodology Statistician at Boehringer Ingelheim

Who should attend?
This statistical conference is addressed to statisticians, pharmacometricians, data scientists, regulatory affairs specialists, academia and other experts interested in the field belonging to: Pharmaceutical, and Biotechnology companies, CROs, Universities/Hospitals, Academic Research.


The Programme is currently under definition

Abstract submission deadline: 31 March 2023
Submit your speaking proposal
Quantifying Uncertainty on Machine Learning-Based Predictive Biomarker Discovery
Konstantinos Sechidis - Associate Director at Novartis Pharma AG

One of the key challenges of personalized medicine is to identify which patients will respond positively to a given treatment.

The area of subgroup identification focuses on this challenge, that is, identifying groups of patients that experience desirable characteristics, such as an enhanced treatment effect.

A crucial first step towards the subgroup identification is to identify the baseline variables (eg, biomarkers) that influence the treatment effect, which is known as predictive biomarkers. When we discover predictive biomarkers it is crucial to have control over the false-positives to avoid waste of resources, as well as provide guarantees over the replicability of our findings.

With our work we introduce a set of methods for controlled predictive biomarker discovery, and we use them to explore heterogeneity in psoriatic arthritis trials.

How much Training Data is needed to Train a Learner? - A Heuristic Approach
Rajat Mukherjee - VP Advanced Statistics and Data Science at Alira Health

One of the most crucial phases of a biomarker discovery or diagnostics development using machine learning (ML) is training the learner algorithm.

A learner is only as good as how well it has been trained in terms of biological variation which depends on the size and the heterogeneity found in the training set.

This on the other hand poses the logistical challenge of planning resources for the training phase. As far as we know, there are no well known approaches to estimating the size of the training data set.

In this talk we restrict our focus on learners where a single dominating predictor can be identified. In this we case we propose and present a simulation based approach to getting a ball-park estimate on the size of the training data.

We also present a seamless adaptive design where the training and the validation of a ML based diagnostic device can be carried out in an operationally seamless fashion while mitigating several risk factors that arise naturally in these kinds of biomedical problems.

Jens-Otto Andreas
Info Scientific Board

Jens-Otto Andreas

SSI Business Operations Excellence Senior Lead at UCB Biosciences GmbH
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
Info Scientific Board

Lisa Comarella

Senior Director Biostatistics at Alira Health Biometrics
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.
Marco Eigenmann
Info Scientific Board

Marco Eigenmann

Principal Biostatistician at Novartis
Marco Eigenmann started his career in the pharmaceutical industry joining Novartis in 2020 as a Principal Biostatistician in the Immunology, Hepatology and Dermatology (IHD) department. In his role, Marco has been supporting several programs in late phase development (phase 2b to phase 4) across the Dermatology and Rheumatology therapeutic areas. Marco holds a Master degree in mathematics from ETH Zurich and has a PhD in statistics from the same university. During his PhD Marco specialized in causal inference and graphical models. Marco’s current interests include the development and standardization of causal methodologies in drug development, effective scientific communication using interactive tools such as R Shiny, and machine learning.
Victoria Strauss
Info Scientific Board

Victoria Strauss

Senior Principal Therapeutic Area Methodology Statistician at Boehringer Ingelheim
Dr Victoria Strauss is a senior Principal Therapeutic Area Methodology Statistician in Boehringer-Ingelheim, and a honorary research in University of Oxford. She specifies in real world evidence methodology including incorporating real world evidence methodology into clinical trials, propensity score, trial emulation, bias minimization in real world data. Prior to join BI, she was the lead Statistician in pharamaco and device epidemiology research group in Centre for Statistics in Medicine, University of Oxford. She is a faculty member of “ISPE pre-conference course: machine learning ” and was a co-lead in the UK NIHR routine data SIG.
Konstantinos Sechidis
Info Speaker

Konstantinos Sechidis

Associate Director at Novartis Pharma AG
Konstantinos (Kostas) is an Associate Director of Data Science in Novartis’ Advanced Exploratory Analytics group and his main areas of interest are machine learning based biomarker discovery, subgroup identification, and development of digital endpoints. He obtained his PhD in statistical machine learning from the Department of Computer Science of the University of Manchester. Afterwards he spent many years as post-doctoral researcher on developing novel methodologies for analysing: self-reported epidemiological data with Manchester’s Health e-Research Center, clinical trials data for personalised medicine with AstraZeneca and digital healthcare data for digital biomarker development with Roche. He is member of the editorial board of the Machine Learning Journal (MLJ) and vice-chair of the technical committee on Statistical Pattern Recognition Techniques of the International Association for Pattern Recognition (IAPR) and more information about his work can be found at:
Rajat Mukherjee
Info Speaker

Rajat Mukherjee

VP Advanced Statistics and Data Science at Alira Health
Biography available soon

Valeria Quintily
Project and Scientific Manager

Ilaria Butta
Events and Training Manager

Quote di iscrizione

€ 590,00* Early Bird fee until 17 October 2023
€ 710,00* Ordinary fee
€ 410,00* Freelance, Academy, Public Administration

Fee includes: seat at the conference, copy of presentations of Speakers who allow the distribution, networking lunch, coffee breaks, organisational office assistance, certificate of attendance.

* for Italian companies: +22% VAT

Informazioni utili

LS Academy is aware of the evolving impact of COVID-19 and is committed to offering safe and secure face-to-face courses and conferences. From physical distancing, protect, detect, cleaning and hygiene.  LS Academy ensures that all our events are conducted in accordance with official government guidelines and regulations, understanding that these measures may vary and change as the situation evolves.

Edizioni Passate
Statistical Reasoning in Drug Development
Application of Causal Inference in Drug Development
Data Science and the Rise of New Analytical Techniques. The Evolution of the Clinical Development Paradigm and Biostatistics
Statistical Methodology for the Assessment and Analysis of Risk and Safety Data in Clinical Development
Innovative Clinical Trial Designs
Statistical Methods for Rare Diseases and Special Populations
Estimand and Missing Value
Applications of Statistical Methodology in Early Drug Development
Diventa Sponsor Versione Stampabile
<p style="text-align: center;"><strong>Munich &#8211; Germany<br />
</strong><em>Conference location TBA</em></p>

Munich – Germany
Conference location TBA

<p style="text-align: center;"><strong>Munich &#8211; Germany<br />
</strong><em>Conference location TBA</em></p>