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 - Therapeutic Area Methodology Chapter Head 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

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.

Precision Medicine: a Whistle-Stop Tour before Designing a Trial with a Biomarker Threshold Optimization!
Guillaume Desachy - Statistical Science Director at AstraZeneca

Anel Mahmutovic, Early Biometrics & Statistical Innovation, Data Science & Artificial Intelligence, R&D, AstraZeneca, Gothenburg, Sweden
Beatriz Seoane Nuñez, Early Biometrics & Statistical Innovation, Data Science & Artificial Intelligence, R&D, AstraZeneca, Barcelona, Spain
Sofia Tapani, Early Biometrics & Statistical Innovation, Data Science & Artificial Intelligence, R&D, AstraZeneca, Gothenburg, Sweden

The term biomarker being widely used, one tends to think that its meaning is well-understood. But it is not always the case. What is a biomarker actually? This session will (re-)introduce biomarkers and will showcase an early phase case study.

In early phase trials, the number of scientific questions that can be addressed is limited by resource constraints. Such questions could be about treatment efficacy, superiority vs. the competition, dosefinding or biomarker threshold fine-tuning.

The development of a compound initially started as a PhIIa. As interest grew, the study design became
a seamless PhII, with a composite time-to-event primary endpoint.

In this study, the biomarker is continuous. Finding the biomarker threshold (BT) that will identify
patients most likely to benefit from the new compound is important. To define a BT, one can start
with a provisional BT and then optimize it. For BT optimization, one needs patients with biomarker
values above and below the BT (respectively BM-high and BM-low).

Simulations were used to assess the number of patients needed for BT optimization. Using a stepfunction,
having as many BM-low as there are BM-high patients in each treatment arm is sufficient to
optimize the BT within a range of clinically meaningful values. The study was designed, approved
internally and by the FDA with this number of patients.

While the study was ongoing, recruitment rates did not meet our expectations. We re-designed the
study and will be able to answer most of the initial scientific questions, including the biomarker
threshold optimization!

AI/ML and Omics Methods and Applications for Precision Medicine
Lin Li - US Head of Statistics and Data Science at PharmaLex

The promise of precision medicine is to deliver the right medicines to the right patients at the right time with the right doses.

The use of biomarkers and omics technologies provides useful tools to help realize this promise by understanding the heterogeneity in people’s genes and associating that with disease status, progression patterns, and response to treatments. For example, molecular profiling of cancers informs more precise subtypes of cancers and variation of immunophenotyping while pharmacogenomics unravels genetic associations with drug response. The interdisciplinary research of precision medicine calls for statisticians to be equipped with advanced analytical strategies and AI/ML methods while have working knowledge in clinical development and molecular biology.

In this talk, I will provide an overview of recent developments of AI/ML and omics data science applied to precision medicine across the entire lifecycle of medical product development. I will use examples of genetic risk score to demonstrate its use in drug discovery and development. I will also discuss data and statistical challenges in precision medicine and provide an outlook for the positive role statisticians can play in accelerating drug development.

Precision Medicine using Polygenic Scores: Leveraging Large-scale Genetic Studies to Enhance Clinical Trials
Oliver Pain - Biostatistician, SSI Predictive Analytics at UCB Pharma

Oliver Pain (1), Kevin Ray(1), Karim Malki(1), Eva Krapohl(1)
1. Predictive Analytics, Statistical Sciences and Innovation, UCB Pharma, Slough, UK

Disease heterogeneity and interpatient variability contribute to differences in drug efficacy and safety – making drug development risky and costly. These individual differences are partly explained by genetic variation. Polygenic scoring is an approach for calculating an individual’s likelihood of a given outcome, leveraging data from large-scale genome-wide association studies (GWAS). Previous research supports incorporating polygenic scores into clinical trials as a biomarker to improve statistical power of the study, reduce costs, and provide novel and personalised therapeutics for patients.

Within the Predictive Analytics Team at UCB’s Statistical Sciences & Innovation department, we have developed a platform for incorporating an individual’s genetic information into the clinical trial design and analysis. The genome-wide genetic data required costs €50 − €100 per person, which once collected can simply be uploaded to our polygenic scoring platform. We then leverage the vast library of genetic associations from publicly available GWAS to calculate a range of polygenic scores that may be relevant to the outcome of interest in the clinical trial. To optimise the polygenic scores, we use Bayesian machine learning methods that capitalise on the ‘polygenic’ nature of how genetics influences disease risk. Our platform then tests whether the polygenic scores moderate the effect of the trial intervention and provides clinical and commercial insights for the design of future clinical trials.

We have applied this analysis framework to data from a clinical trial at UCB. Across all individuals, there was no effect of the drug on the clinical trial outcome, but we find evidence that polygenic scores moderate response to the drug in trial, identifying a subgroup of patients who showed significant improvements in symptoms compared to placebo.

This use case example supports the notion that incorporating polygenic scores into clinical trials can provide valuable insights into specific patient groups that will benefit from a given medication, helping to optimise clinical trials and provide personalised medicine. We present polygenic scores as a novel cost-effective biomarker for safer and more efficacious drug profiles with the potential to facilitate the drug approval process, reducing time to market and accelerating personalised medicine.

The Population-wise error rate for Cinical Trials with Multiple intersecting Sub-populations
Werner Brannath - Faculty of Mathematics and Computer Science & Competence Center for Clinical Trials Bremen at University Bremen

Studies in precision medicine often lead (either explicitly or implicitly) to simultaneous efficacy testing in multiple overlapping subpopulations. To limit the likelihood of false-positive conclusions, some form of control for multiple error rates is often recommended or required. Because strict control of family-wise error rate may be too conservative when the number of subpopulations is large and/or the sample sizes per subpopulation are small, less conservative approaches to multiple testing are desirable.

This presentation introduces and discusses the recently proposed concept of the population-wise error rate (PWER). The PWER controls for the average risk that a randomly selected future patient (drawn at random from the trial’s target population) will receive an ineffective treatment as a result of the trial’s test results. In this multiple error rate concept, only the type I errors relevant to the subpopulation are considered. As an average of the stratified multiple error rates, the population-wise error rate is smaller than the family-wise error rate and thus less conservative.

We will illustrate the new concept with several examples, discuss its advantages and limitations, and investigate the gain in informativeness compared to controlling for the family-wise error rate through simulation studies.

The presentation will be based on https://journals.sagepub.com/doi/full/10.1177/09622802221135249 and more recent research findings.

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

Therapeutic Area Methodology Chapter Head at Boehringer Ingelheim
Dr Victoria Strauss is TAM Chapter Head, Global Biostatistics & Data Science 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.
Werner Brannath
Info Speaker

Werner Brannath

Faculty of Mathematics and Computer Science & Competence Center for Clinical Trials Bremen at University Bremen
Biography available soon
Guillaume Desachy
Info Speaker

Guillaume Desachy

Statistical Science Director at AstraZeneca
Since graduating from ENSAI (Biostatistics M. Sc.) 10 years ago, Guillaume has been immersing himself in precision medicine. Data-driven, he is passionate about answering scientific questions and making sure we convey the right message to stakeholders, both internally & externally. He feels very fortunate to have had the chance to work with various kinds of OMICs data and leverage the power of biomarkers to strengthen drug development. He also feels incredibly lucky to have worked in a diverse set of settings, be it in academia (UCSF, U.S.), in a biotech (Enterome, France) or in the pharmaceutical industry (BMS, Servier & AstraZeneca, France & Sweden). He now works as a Statistical Science Director for AstraZeneca in Gothenburg, Sweden. Apart from his day job at AstraZeneca, Guillaume teaches a course about OMICs data analysis at ENSAI (www.ensai.fr), is actively involved in the ENSAI alumni association (www.ensai.org) and is a mentor for Article 1, a non-profit organization promoting equal opportunity (https://article-1.eu/) and together with Nicole Krämer, he leads the EFSPI/PSI Biomarkers Special Interest Group.
Lin Li
Info Speaker

Lin Li

US Head of Statistics and Data Science at PharmaLex
Dr. Li is a statistician and computational biologist by training with more than twelve years of work experience in statistical methodology development and analysis in the fields of biomarker biostatistics, precision medicine, and statistical genomics. His expertise includes biomarker statistics and omics data science with applications in drug discovery and development. Some of his work has been published in high profile journals. Dr. Li is US Head of Statistics and Data Science at PharmaLex, a global specialized service provider for the pharma, biotech, and MedTech industries. He received his doctoral degree in computational biology from Cornell University and was a Postdoctoral Research Fellow in the Department of Biostatistics at Harvard School of Public Health.
Rajat Mukherjee
Info Speaker

Rajat Mukherjee

VP Advanced Statistics and Data Science at Alira Health
Biography available soon
Oliver Pain
Info Speaker

Oliver Pain

Biostatistician, SSI Predictive Analytics at UCB Pharma
Oliver Pain is a biostatistician within the Statistical Sciences and Innovation Predictive Analytics team at UCB Pharma. He also holds a Sir Henry Wellcome Postdoctoral Research Fellowship at King’s College London. Oliver’s research mainly leverages results from genome-wide association studies (GWAS) and Bayesian machine learning methodology to calculate ‘polygenic scores’ predicting complex health-related outcomes. Currently at UCB, Oliver and colleagues have developed the ‘Polygenic Score Knowledge Base’ - a platform for integrating genetic data into clinical trials to improve their efficacy and efficiency.
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: https://sechidis.netlify.app/

Valeria Quintily
Sr. Scientific Project Manager

Ilaria Butta
Events and Training Manager

Registration Fee

€ 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

Useful Information

The conference will take place at:

NH Collection München Bavaria
Arnulfstr. 2, 80335 Munich

The NH Collection München Bavaria is located in the city center next to the main train station, and only steps away from the old town, it offers easy access to all of Munich, making it an ideal base for business visitors.

From the airport
Train: Take the S1 or S8 from the Munich Airport, which departs every 5 – 10 minutes, to get to the central station “München Hauptbahnhof”. From there, please alight in the direction of Arnulfstraße. The hotel is located on the opposite side of the street. Total trip time is around 45 minutes.
Taxi: It is a 30-40 minutes trip

From the train station
München Hauptbahnhof: From there, please alight in the direction of Arnulfstraße. The hotel is located on the opposite side of the street.
Closest metro station: München Hauptbahnhof

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.

Past Conferences
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
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<p style="text-align: center;"><strong>NH Collection München Bavaria</strong><br />
Arnulfstr. 2<br />
80335 Munich &#8211; Germany</p>

NH Collection München Bavaria
Arnulfstr. 2
80335 Munich – Germany

<p style="text-align: center;"><strong>NH Collection München Bavaria</strong><br />
Arnulfstr. 2<br />
80335 Munich &#8211; Germany</p>
<p style="text-align: center;"><strong>NH Collection München Bavaria</strong><br />
Arnulfstr. 2<br />
80335 Munich &#8211; Germany</p>
<p style="text-align: center;"><strong>NH Collection München Bavaria</strong><br />
Arnulfstr. 2<br />
80335 Munich &#8211; Germany</p>