Track: Track 2
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.
Public lecture
Chairs: Werner Brannath and Andreas Faldum
Zeiten
ab 17:00 Uhr: Einwahl in den Zoom-Raum möglich
18:00 – 19:30 Uhr: Vortrag (auf deutsch/in German)
PD Dr. Benjamin Hofner, Leiter des Fachgebiets Biostatistik des Paul-Ehrlich-Instituts Langen, Bundesinstitut für Impfstoffe und biomedizinische Arzneimittel; Lehrbeauftragter der Universität Erlangen-Nürnberg.
Thema: „Statistik in Zeiten von Corona – Der komplexe Weg zur Zulassung eines Impfstoffes“
Statistik in Zeiten von Corona – Der komplexe Weg zur Zulassung eines Impfstoffes
Benjamin Hofner, Paul-Ehrlich-Institut, Bundesinstitut für Impfstoffe und biomedizinische Arzneimittel, Langen.
Die schnelle Entwicklung eines wirksamen und sicheren Impfstoffes während einer Pandemie ist eine unglaubliche Herausforderung. Wie man im vergangenen Jahr in den Hauptnachrichten verfolgen konnte besteht diese Herausforderung nicht nur in der Entwicklung des Impfstoffes im Labor sondern auch in der darauf folgenden Testung und Zulassung des Impfstoffes.
Dieser Vortrag beleuchtet die klinische Entwicklung, in der letzten und entscheidenden Phase 3 Studie. Exemplarisch geht er dabei auf die Studien der zwei bereits zugelassenen Impfstoffe von BioNTech/Pfizer und Moderna ein. Er stellt die zentrale Rolle der statistischen Aspekte (z.B. Studiendesign, Fallzahlplanung, Studienpopulation, Endpunkte) sowohl bei der Planung als auch bei der Auswertung der Studien heraus. Statistische Konzepte und deren Notwendigkeit in Impfstoffstudien sollen dabei auch für interessierte Laien verständlich erklärt und motiviert werden. Außerdem werden regulatorische Abläufe die zur Zulassung der Impfstoffe führen aufgezeigt.
Zeit: Sonntag, 14. März 2021, 18:00 Uhr – 19:30 Uhr
Short CV
PD Dr Benjamin Hofner is Head of the Section Biostatistics at the Paul-Ehrlich-Institute, the German Federal Institute for Vaccines and Biomedicines. He provides input to the Biostatistics Working Party (BSWP) of the European Medicines Agency (EMA). He is member in several BSWP task forces, including a group working on guidance for studies for the treatment and prevention of COVID-19.
Dr Hofner graduated in Statistics from the LMU Munich in 2008. In 2011 he obtained his PhD in Statistics from the LMU Munich for his work on statistical approaches to machine learning. In 2018 he received his Venia Legendi (“Privatdozent”) in Biostatistics from the University Erlangen-Nuremberg.
Dr Hofner’s current research interests mainly focus on innovative clinical trial designs and other statistical issues in the field of “regulatory biostatistics”. He is Task Lead in the EU-funded IMI project EU-PEARL on patient-centric platform trials and Work Package Lead in the IMI project COMBINE on anti-microbial resistance. Besides his duties at the Paul-Ehrlich-Institute, he is Adjunct Lecturer for Biostatistics at the medical school of the University Erlangen-Nuremberg.
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.
Literature:
– 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).