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
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.
All the below mentioned times are CET
Ariel Alonso Abad
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
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.
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.
Statistical inference, multivariate analysis.
Who should attend?
The course is addressed to Statisticians, health professionals with statistical background, master and PhD students.
Lectures with some practical sessions/examples.
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.
Imbens G., Rubin D.B. (2015) Causal Inference for the Statistics, Social and Biomedical Sciences: An Introduction, Cambridge University Press
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.
€ 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.
€ 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