Introduzione
In 2020 the revision of ICHE9 has became effective in the ICH regions.
Beyond the immediate operational consequences in terms of study documentation development, it has become clearer and clearer that the introduction of the Estimand framework may require methodological solutions which were not common practice in drug development when the revised guidance was released.
In this context, causal inference, or the problem of causality in general, has received a lot of attention in recent years. It appears to provide a suitable methodological framework for tackling some of these challenges.
Five years from now the event touched on Estimands. In this year's edition, the ESF goes back to Estimands to better understand the implications of the introduction of the ICHE9 (R1) guidance in terms of how the traditional approach of statistical inference may be affected in its fundamentals of sample size, testing, and estimation by the arrival of causal inference.
The conference will focus, among others, on the following areas:
- Regulatory perspective on study design development based on causal inference methods
- Operational implementation of causal inference methods
- Novel approaches based on causal inference methodologies
- Real case studies and practical approaches
Scientific Board
Jens-Otto Andreas - Project Lead Statistician 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
All the below mentioned times are CET
Relatori
Jens-Otto Andreas
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 BSO Business Operations Excellence Senior Lead at UCB Biosciences GmbH.
Lisa Comarella
Lisa has over 20 years of experience in the clinical research industry. In her current role, Lisa is supporting the management of the biostatistics team including the development professional growth, supervision of the quality of deliverables and outputs, as well as the oversight 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 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.
Giacomo Mordenti
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
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
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
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
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
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
After beeing a Professor of Statistics and Machine Learning at Ecole Polytechnique (IPP, institut Polytechnique de Paris) and a visiting researcher at Google Brain Paris, Julie Josse has just joined Inria as a senior researcher to set up a team in data-sciences for health. Julie Josse’s is an expert in handling missing values and has created a platform to collect works and give resources to users. Her curent research focuses on causal inferences techniques for personalized medicine. She leads a project with the Traumabase group dedicated to the management of polytraumatized patients. Julie Josse is dedicated to reproducible research with the R statistical software: she has developed packages including FactoMineR to transfer her work, she is a member of the R foundation and of Rforwards to increase the participation of minorities in the community.
Fabrizia Mealli
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
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
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 02 November 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 02 November 2021
€ 590,00* 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 02 November 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
Registrati
Versione Stampabile
Media Partner
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.