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.
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.
We’re working on the programme.
New details soon
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
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.
€ 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.
€ 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