Location: Zoom-Session

IBS-DR Mitgliederversammlung

Vorsitz: IBS-DR Vorstand

Tagesordnung der Mitgliederversammlung 2021

TOP 1 Verabschiedung der Tagesordnung Brannath
TOP 2 Genehmigung des Protokolls der Mitgliederversammlung vom 09.09.2020 Scharpenberg
TOP 3 Bericht des Präsidenten Brannath
TOP 4 Nachwuchspreise Brannath
TOP 5 Berichte aus den internationalen Gremien Bretz, Ickstadt, Kieser, Kübler, Pigeot, Ziegler
TOP 6 Bericht des Schriftführers Scharpenberg
TOP 7 Bericht aus der Geschäftsstelle Scharpenberg
TOP 8 Bericht des Schatzmeisters Knapp
TOP 9 Bericht der Kassenprüfer Dierig, Tuğ
TOP 10 Beschlüsse über Rückstellungen und Mitgliedsbeiträge 2022 Knapp
TOP 11 Berichte aus den Arbeitsgruppen Asendorf
TOP 12 Sommerschulen, Weiterbildung Brannath
TOP 13 Zukünftige Kolloquien Brannath
TOP 14 Biometrical Journal Bathke, Schmid
TOP 15 Bericht des Wahlleiters über die Beiratswahl Gerß
TOP 16 Verschiedenes Brannath

Opening Session / Keynote: Machine Learning in Biometry

Chairs: Werner Brannath and Katja Ickstadt

Speakers: Andreas Faldum (Conference president) , Frank Müller (Dean of the Medical Faculty), Werner Brannath (President of the IBS-DR), Markus Lewe (Mayor, City of Münster) || Keynote speaker: Chris Holmes

Title: Machine Learning in Biometrics
Chris Holmes

Machine learning (ML) and artificial intelligence (AI) have had a major impact across many disciplines including biometrics. In the first half of this talk we will review some of the characteristics of ML that make for successful applications and also those features that present challenges, in particular around robustness and reproducibility. Relatively speaking, ML is mainly concerned with prediction while the majority of biometric analyses are focussed on inference. In the second half of the talk we will review the prediction-inference dichotomy and explore, from a Bayesian perspective, the theoretical foundations on how modern ML predictive models can be utilised for inference.

rpact R Package for Adaptive Confirmatory Trials

Login details (Zoom Session ID and password) will be sent to registered participants by email until March 10th.
If you are registered and did not receive this email until March 10th, please contact us at biomkoll@uni-muenster.de

Tutorium rpact R Package for Adaptive Confirmatory Trials” (half-day), Gernot Wassmer

– Concept of the Software
– Methodology and Basis Features of the Software
– Shiny App / Vignettes
– Designing Adaptive Trials

Short CV
Gernot Wassmer has a diploma in Statistics and received his PhD from the University of Munich, Germany in 1993, after which he was a Research Fellow at Munich’s Institute of Statistics, at the Institute for Epidemiology, GSF Neuherberg, and at the Institute of Medical Statistics, University of Cologne, Germany. From 2010 to 2014, he worked at Aptiv Solutions and Icon in Cologne. He is an Adjunct Professor of Biostatistics at the Institute of Medical Statistics, University of Cologne, and founder and owner of RPACT GbR. His major research interest is in the field of statistical procedures for group sequential and adaptive plans in clinical trials. He has been a member of independent data monitoring committees for international, multi-center trials in various therapeutic fields and also serves as a consultant for the pharmaceutical industry.

Introduction to Machine Learning with R

Login details (Zoom Session ID and password) will be sent to registered participants by email until March 10th.
If you are registered and did not receive this email until March 10th, please contact us at biomkoll@uni-muenster.de

Tutorial “Introduction to Machine Learning with R” (half-day, morning), Przemyslaw Biecek

The agenda:
– The two cultures – To predict or to understand
– Evaluation of black-box models (RMSE, AUC, ROC, F1, LIFT, train-test split, CV-folds)
– Towards complex models – bias variance tradeoff (random forest, bagging, boosting)
– Explanatory Model Analysis – global level (partial dependence profiles, feature importance)
– Explanatory Model Analysis – local level (Shapley values, ICE/CP)
– The multiplicity of good models – Rashomon effect 
– From complex to interpretable – how to extract features from black-box models

With a hands-on part based on R examples with medical examples.

– Introduction to Statistical Learning: http://faculty.marshall.usc.edu/gareth-james/ISL/
– Explanatory Model Analysis: https://github.com/pbiecek/ema
– mlr3 book: https://mlr3book.mlr-org.com/

Short CV

Przemyslaw Biecek graduated in Mathematical Statistics and Software Engineering from Wroclaw University of Technology. He obtained his PhD in biostatistics, after which he held positions at Hasselt University (Belgium), Davis University (USA) and Nanyang Technological University (Singapore). He is now an Associate Professor in Machine Learning at Warsaw University of Technology and University of Warsaw.
His main areas of research include model visualization, model interpretability and predictive modelling of large and complex data with a special focus on applications in personalized computational medicine/oncology. He is also interested in evidence-based medicine and statistical software engineering (an R enthusiast).

An Introduction to Causal Inference and Target Trials

Login details (Zoom Session ID and password) will be sent to registered participants by email until March 10th.
If you are registered and did not receive this email until March 10th, please contact us at biomkoll@uni-muenster.de

Tutorium “An Introduction to Causal Inference and Target Trials” (full-day), Sonja Swanson

– Basic concepts and assumptions of causal inference, using counterfactual or potential outcomes
– Key sources of bias: e.g., confounding, selection, and information bias
– Describing a target trial: describing the key protocol elements of an ideal randomized trial, including the eligibility criteria, treatment strategies, treatment assignment, follow-up period, outcome, causal contrast, and statistical analysis
– Emulating a target trial: designing and analyzing observational data to estimate causal effects, including the use of g-methods

Cain LE, Saag MS, Petersen M, May MT, Ingle SM, Logan R, et al. Using observational data to emulate a randomized trial of dynamic treatment-switching strategies: an application to antiretroviral therapy. Int J Epidemiol. 2016; 45(6):2038–49.

García-Albéniz X, Hsu J, Hernán MA. The value of explicitly emulating a target trial when using real world evidence: an application to colorectal cancer screening. Eur J Epidemiol. 2017. doi:10.1007/s10654-017-0287-2.

Hernán MA, Robins JM. Using big data to emulate a target trial when a randomized trial is not available. Am J Epidemiol. 2016; 183(8):758–64.

Labrecque, J.A., Swanson, S.A. Target trial emulation: teaching epidemiology and beyond. Eur J Epidemiol. 2017; 32, 473–475.

Short CV
Sonja Swanson is an Assistant Professor in the Department of Epidemiology at Erasmus MC and holds an adjunct affiliation with the Department of Epidemiology at the Harvard T. H. Chan School of Public Health. She was recently invited to join the editorial team of Epidemiology. Her methodological research focuses on improving the use and transparency of methods for estimating causal effects in epidemiology. This work spans applications in observational studies and pragmatic randomized trials. She has made important contributions to instrumental variables methods, e.g. in the context of Mendelian randomization, as well as target trials, effect heterogeneity or causal mediation. Her substantive research primarily focuses on neuropsychiatric disorders and related health outcomes, including using appropriate methods to studying potential prevention and treatment strategies.