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

We’re working on the programme.
New details soon

Causal inference in drug development – a regulatory perspective
Theodor Framke - Seconded National Expert at European Medicines Agency

Abstract available soon

An information-theoretic approach for the evaluation of surrogate endpoints based on causal inference
Ariel Alonso Abad - Professor at KUleuven

The individual causal association (ICA) is a surrogacy metric designed to assess the validity of a binary outcome as a putative surrogate for a binary true endpoint. The ICA is based on two pillars:

i) Information theory and

ii) a bivariate causal inference model for a binary surrogate and true endpoint.

The ICA has a simple and appealing interpretation in terms of uncertainty reduction. The identifiability issues inherent to the use of causal inference models are tackled using a two-step procedure.

In the first step, the region of the parametric space of the distribution of the potential outcomes, compatible with the data at hand, is geometrically characterized.

Further, in a second step, a Monte Carlo approach is proposed to study the behavior of the ICA on the previous region. The method is illustrated using data from the Collaborative Initial Glaucoma Treatment Study.

A newly developed and user-friendly R package Surrogate is provided to carry out the evaluation exercise.

Rephrasing Least Squares Means as a causal quantity
Christian Pipper - Senior Statistical Advisor at LEO Pharma A/S

When reporting “average” values of continuous endpoints measured at end of trial in a parallel arm RCT the preferred measure is the Least Squares Means. But what is the actual interpretation of this estimated average?

In this talk we argue that Least Squares Means may actually be viewed as estimates of average potential outcomes. Specifically, we show how the least squares means may be identified as such and estimator via the G-computation formula (Robins, 1986).

Besides offering a formalized interpretation of LSMEANS, the above characterization also highlights a somewhat overlooked issue with standard errors of LSMEANS supplied by most standard software. We argue that when models for analysing the endpoint contains covariates like a baseline measurement of the endpoint, then the usual standard error is systematically too low. We finally show how the G-computation framework facilitates correct estimation of the standard error in these instances. All developments are exemplified with a case study assessing QTc prolongation based on LSMEANS.

Robins J. A new approach to causal inference in mortality studies with a sustained exposure period — application to control of the healthy worker survivor effect. Math Model. 1986;7(9–12):1393–1512.

Hypothetical estimands in clinical trials - a unification of causal inference and missing data methods
Jonathan Bartlett - Reader in Statistics at University of Bath

In diabetes trials some patients may require rescue medication during follow-up. If the level of rescue medication use differs between treatment groups, a treatment policy / intention to treat analysis may be difficult to interpret. Here a hypothetical estimand which targets the effect that would have been seen had rescue medication not been available may be of interest to some stakeholders. In this talk I will discuss statistical methods for estimation of such hypothetical estimands. I will first describe hypothetical estimands using the causal inference concepts of potential outcomes, before using the existing causal inference machinery to describe what assumptions are needed to estimate hypothetical estimands. In particular this will allow us to be clear about what variables need to be adjusted for to estimate hypothetical estimands. I will then discuss both ‘causal inference’ and ‘missing data’ methods (such as mixed models) for estimation, and show that in certain situations estimators from these two sets are in fact identical. These links may help those familiar with one set of methods but not the other. They may also identify situations where currently adopted estimation approaches may be relying on unrealistic assumptions, and suggest alternative approaches for estimation.

Principal Stratum Estimands in Drug Development
Björn Bornkamp - Senior Director Statistical Consultant at Novartis Pharma AG
Baldur Magnusson - Senior Director Biostatistics at Novartis Pharma AG

Questions on the treatment effect in sub-populations defined by post-randomization events are not uncommon in drug development. In the causal inference literature estimands of this type are typically referred to as principal stratum estimands.

As the population is defined based on events that may be influenced by the received treatment, randomization alone can no longer be relied upon for assessment of the treatment effect. Additional assumptions are required to identify the estimand.

In this presentation we will review examples from drug development, where principal stratum estimands could be of interest, review estimation strategies and provide a concrete recent example in a multiple sclerosis development program.

Causal inference for obstervational clinical data
Julie Josse - Senior Researcher in Statistics, Machine Learning

Abstract available son

Relatori
Jens-Otto Andreas
Info Scientific Board

Jens-Otto Andreas

Project Lead Statistician 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 Project Lead Statistician at UCB Biosciences GmbH. 
Lisa Comarella
Info Scientific Board

Lisa Comarella

Director Biostatistics at CROS NT
Lisa Comarella joined CROS NT in 2000 as a Statistician and Data Manager. In the past 19 years, she has developed a career in CROS NT as a Principal Statistician and since 2010 has been managing the team of biostatisticians as Director of Biostatistics. In her role as head of the department, Lisa is responsible for the management of statistics resources, supervision of the quality of deliverables, and contributing to complex and strategic projects as statistical lead. Lisa holds a Master’s degree in Statistics and Demographic Sciences from the University of Padua in Italy. She is a scientific committee chair for ESF – the European Statistical Forum, she is a member of associations of statisticians in Europe (PSI, EFSPI).
Giacomo Mordenti
Info Scientific Board

Giacomo Mordenti

Head of Biostatistics & Data Management Europe at Daiichi Sankyo Europe GmbH
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
Info Scientific Board

Marc Vandemeulebroecke

Global Group Head for Dermatology at Novartis Biostatistics
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.
Ariel Alonso Abad
Info Speaker

Ariel Alonso Abad

Professor at KUleuven
Prof Alonso obtained master degrees in Mathematics and Biostatistics from the universities of Havana (Cuba) and Hasselt (Belgium) in 1997 and 1999, respectively. In 2004, he received his Phd degree in Biostatistics from Hasselt University in Belgium. Prof Alonso has worked as a consultant at the National Coordination Center for Clinical Trials in Cuba, as Assistant  Professor at the University of Maastricht in the Netherlands and as Associate Professor at the KU Leuven in Belgium. He has published methodological research on the  validation of surrogate markers, the assessment of rating scales and inference under misspecification in hierarchical models. He is also a coauthor of the freely available R package Surrogate that implements several validation strategies for surrogate  endpoints, within the  causal-inference and meta-analytic frameworks
Jonathan Bartlett
Info Speaker

Jonathan Bartlett

Reader in Statistics at University of Bath
Jonathan Bartlett is a Reader in Statistics at the University of Bath, UK, having held previous positions at AstraZeneca’s Statistical Innovation Group and the London School of Hygiene & Tropical Medicine. His research focuses on developing statistical methods for handling missing data and more recently on estimation of estimands in clinical trials. He maintains a statistics blog at https://thestatsgeek.com
Björn Bornkamp
Info Speaker

Björn Bornkamp

Senior Director Statistical Consultant at Novartis Pharma AG
Björn Bornkamp works in the Statistical Methodology Group of the Advanced Methodology and Data Science team at Novartis in Basel, where he provides consulting to statisticians and clinical teams on topics related to dose-finding studies, subgroup analyses, Bayesian statistics as well as estimands and causal inference.
Theodor Framke
Info Speaker

Theodor Framke

Seconded National Expert at European Medicines Agency
Theodor studied statistics at the Dortmund University of Technology and at the University of Auckland. He joined the Institute of Biostatistics at Hannover Medical School in 2009 where his main areas of work were consulting, teaching and clinical trials. He also served as a deputy member for the Ethics Committee at Hannover Medical School and completed a PhD in Biostatistics. Theodor works as a Seconded National Expert at the EMA since September 2020.
Julie Josse
Info Speaker

Julie Josse

Senior Researcher in Statistics, Machine Learning
Biography available soon
Fabrizia Mealli
Info Speaker

Fabrizia Mealli

Professor of Statistics, Director of the Florence Center for Data Science at University of Florence
Fabrizia Mealli is Professor of Statistics. Her research focuses on causal inference, program evaluation, estimation techniques, simulation methods, missing data, and Bayesian inference, with applications to the social and biomedical sciences. She held visiting positions at Harvard University, UCLA, LISER Luxembourg. She serves as coordinator of the Statistics track for the PhD program in Mathematics, Computer Science, Statistics of the University of Florence, and sits the Steering Committee of the European Causal Inference Meeting. She is Elected Fellow of the American Statistical Association, and currently an associate editor of “The Annals of Applied Statistics” and “Observational Studies”.
Baldur Magnusson
Info Speaker

Baldur Magnusson

Senior Director Biostatistics at Novartis Pharma AG
Baldur is a Global Group Head in Early Development Analytics at Novartis, leading a team that provides biostatistics support in non-clinical, digital, and exploratory biomarkers. Previous roles at Novartis include project statistician in early clinical development and consultant in the Advanced Methodology and Data Science team. Baldur has been involved in several methodological initiatives, focusing on topics such as visualization, dose finding, historical data, and causal inference. Baldur holds a PhD in statistics from Cornell University.
Christian Pipper
Info Speaker

Christian Pipper

Senior Statistical Advisor at LEO Pharma A/S
CBP has a PhD degree in mathematical statistics specializing in Survival analysis. He is currently working as statistical methodology advisor at LEO Pharma A/S. Before joining LEO cbp has worked as Associate Professor of Biostatistics at University of Copenhagen for almost 10 years, doing research within survival analysis, causal inference, adaptive designs, and multiple testing. He is Adjungated professor of Biostatistics at University of Southern Denmark, Associate Editor of International Journal of Biostatistics, member of the Estimand implementation working group under EFPSI, and a board member of the Danish Society of Theoretical Statistics.
Training
Pre-Conference Training
15 November 2021

2.00 pm to 6.00 pm CET – Online

An Introduction to Causal Inference in Experimental and Observational Settings – Theory and practice

 

Introduction
Most scientific questions are causal in nature. It is therefore necessary to introduce a formal causal language to help define causal effects and spell out the assumptions required to infer such effects from experimental and observational data.

Programme
The potential outcome approach to causal inference will be introduced and statistical methods for inferring causal effects from randomized experiments or observational studies will be presented. Examples and practical sessions will be based on case studies in biostatistics, epidemiology, and public health.

  • Introduction to causal inference from the potential outcome perspective.
  • Design and analysis of randomized experiments: Fisher’s exact p-values, estimators of average causal effects, regression, imputation-based approaches.
    • Practical session 1: Analyzing an RCT on the effect of statins on cholesterol
  • Design and analysis of observational studies under confoundedness: the role of the propensity score; matching, weighting, regression estimators.
    • Practical session 2: Analyzing an observational study on the effect of statins
  •  Beyond RCTs. Intercurrent events: challenges and opportunities. Presentation of a case study with discussion.

Lecture notes, slides, data, articles and other reading material will be distributed before the course.
Practical sessions will be in R but no a priori knowledge of R is required.

Participant experience
Statistical inference, multivariate analysis.

Who should attend?
The course is addressed to Statisticians, health professionals with statistical background, master and PhD students.

Teaching methods
Lectures with some practical sessions/examples.

Lecturer
Fabrizia Mealli – Professor of Statistics, Director of the Florence Center for Data Science at University of Florence

Fabrizia Mealli is Professor of Statistics. Her research focuses on causal inference, program evaluation, estimation techniques, simulation methods, missing data, and Bayesian inference, with applications to the social and biomedical sciences. She held visiting positions at Harvard University, UCLA, LISER Luxembourg. She serves as coordinator of the Statistics track for the PhD program in Mathematics, Computer Science, Statistics of the University of Florence, and sits the Steering Committee of the European Causal Inference Meeting. She is Elected Fellow of the American Statistical Association, and currently an associate editor of “The Annals of Applied Statistics” and “Observational Studies”.

At the end of the training, you will be able to …
… develop expertise to assess the credibility of causal claims and the ability to apply the relevant statistical methods for causal analyses.


Main reference:

Imbens G., Rubin D.B. (2015) Causal Inference for the Statistics, Social and Biomedical Sciences: An Introduction, Cambridge University Press

Contatti

Valeria Quintily
Project & Scientific Manager
valeria.quintily@lsacademy.com
+39.329.4683329

Ilaria Butta
Events & Training Executive
ilaria.butta@lsacademy.com
+39.379.1492960


Quote di iscrizione

Pre-Conference Training + Conference:
€ 795,00* Super Early Bird fee until 31 August 2021
€ 835,00* Early Bird fee until 15 October 2021
€ 1.150,00* Ordinary fee
€ 495,00* Freelance, Academy, Public Administration

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

Pre-Conference Training:
€ 440,00* Early Bird fee until 15 October 2021
€ 610,00* Ordinary fee
€ 265,00* Freelance, Academy, Public Administration

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

Conference:
€ 490,00* Early Bird fee until 15 October 2021
€ 630,00* Ordinary fee
€ 290,00* Freelance, Academy, Public Administration

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

* for Italian companies: +22% VAT

Edizioni Passate
2020
Data Science and the Rise of New Analytical Techniques. The Evolution of the Clinical Development Paradigm and Biostatistics
2019
Statistical Methodology for the Assessment and Analysis of Risk and Safety Data in Clinical Development
2018
Innovative Clinical Trial Designs
2017
Statistical Methods for Rare Diseases and Special Populations
2016
Estimand and Missing Value
2015
Applications of Statistical Methodology in Early Drug Development
Diventa Sponsor Versione Stampabile
Media Partner

<p style="text-align: center;"><span>Virtual conference with presentations, slots for Q&amp;A and discussion among delegates.</span><br />
<em>LS Academy will provide the link to join the conference some days before.</em></p>

Virtual conference with presentations, slots for Q&A and discussion among delegates.
LS Academy will provide the link to join the conference some days before.

<p style="text-align: center;"><span>Virtual conference with presentations, slots for Q&amp;A and discussion among delegates.</span><br />
<em>LS Academy will provide the link to join the conference some days before.</em></p>

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