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Statistik: mehr als Erbsen zählen

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Workshop : Retreat

Retreat 2021
Group photo of the members of the RTG, taken at the 2021 retreat.




  • Retreat

    Date and Time: 05.09-06.09.2022

  • Retreat

    Date : 23.09.-24.09.2021

  • Workshop “Joy of P-Splines”

    Speaker : Prof. Brian D. Marx , Department of Experimental Statistics Louisiana State University

    Date : 05.07-06.07.2021


  • Random forests on high-dimensional data: from classification and survival analysis to generative modelling

    Speaker: Prof. Dr. Marvin N. Wright, Leibniz-Institut für Präventionsforschung und Epidemiologie – BIPS, Bremen

    Date and Time: 04.07.2022

  • Issues in supervised prediction and classification for complex chemical substances

    Speaker: Prof. Fred Wright, Ph.D., Departments of Statistics and Biological Sciences, North Carolina State University, USA

    Date and Time: 27.06.2022, TU Dortmund, M/E 21

  • Modeling Heterogeneity for Stratified Populations

    (Click to see abstract)

    We frequently encounter stratified data. In precision medicine, patients are exposed to different treatment options. In health disparities research, race, gender, and rurality are key indicators for investigation. In health care management, health outcomes of elderly patients are greatly influenced by presence of their existing chronic conditions. In this talk, I present some strategies to model such stratified data to address interested research questions from my collaboration with medical investigators. The talk will focus on model building in the presence of many covariates rather than computational and theoretical results, which are available in published works. It will cover stratification by one indicator, by two indicators, by three indicators, and if time permits, by many more multilevel indicators.

    Speaker: Prof. Menggang Yu, Department of Biostatistics and Medical informatics, University of Wisconsin-Madison, USA

    Date and Time: 24.06.2022 at 12.00, TU Dortmund, M/E 21

  • Propensity Score: an Alternative Method of Analyzing Intervention Effect.

    (Click to see abstract)

    There is agreement in medical research that the preferred method for evaluating interventions is the randomized controlled trial. Randomization is the only method that guarantees similar distributions of known and unknown patient characteristics between an intervention and a control group thus enabling true causal statements on intervention effects. However, randomized controlled trials are in some cases "unnecessary, inappropriate, impossible, or inadequate" and have also been criticized for a lack of external validity: Patients in randomized controlled trials are usually younger and healthier than the average patient. Non-randomized studies can be an alternative here, however, they suffer from a lack of internal validity: Treatment allocation is not randomized and the intervention and control groups may be systematically different in terms of known and (even worse) unknown patient characteristics. A range of statistical procedures have been developed to take account of these differences during analysis. The standard procedures for this are multiple regression models, however, propensity scores (PS) are also increasingly used. The propensity score is defined as the probability that a patient receives the intervention under investigation. In a first step, the PS is estimated from the available data, e.g. in a logistic regression model. In a second step, the actual intervention effect is estimated with the aid of the PS. In this talk, we give a short, non-technical introduction to the propensity score using an example from coronary bypass surgery.

    Speaker: Prof. Dr. sc. hum. Oliver Kuß, Deutsches Diabetes-Zentrum, Düsseldorf

    Date and Time: 20.06.2022 via Zoom

  • Gene expression to Omics: The evolution of genetic research

    Speaker: Ashtyn Areal, IUF Düsseldorf

    Date and Time: 30.05.2022, TU Dortmund, M/E 21

  • Analysis and design of clinical trials with biologics using dose-time-response models

    Speaker: Markus Lange, Novartis

    Date and Time: 23.05.2022 via Zoom

  • The case time series design for high-dimensional data analyses

    Speaker: Prof. Antonio Gasparrini, London School of Hygiene and Tropical Medicine, London, UK

    Date and Time: 16.05.2022 via Zoom

  • Mehrstadien-Modelle in der Epidemiologie chronischen Erkrankungen

    Speaker: Prof. Dr. rer. nat. Ralph Brinks , Lehrstuhl für Medizinische Biometrie und Epidemiologie, Private Universität Witten/Herdecke gGmbH

    Date and Time: 09.05.2022 at 16:15 via Zoom


  • Can statistics save preclinical research?

    Speaker: Prof. Dr. med. Ulrich Dirnagl, Abteilungsdirektor Experimentelle Neurologie, Charité Berlin

    Date and Time: 06.12.2021 at 16:15 via Zoom

  • Dose-response analysis for gene-expression data

    (Click to see abstract)

    Advances in genomics bring forward increasingly large omics data sets, such that even concentration-resolved gene expression data are available. We transfer well established dose-response theory from clinical research to toxicological gene expression data.

    Multiple-Comparison-Procedure and Modeling (MCP-Mod) is a relatively new dose-response modeling technique developed for Phase II clinical dose-finding trials that accounts for model uncertainty. By applying MCP-Mod on a concentration-resolved gene expression data set, we find that commonly assumed monotonicity is not adequate and model uncertainty should be considered.

    Speaker: Scott S. Auerbach, PhD, und Matt Wheeler, PHD, Institute of Environmental Health Sciences, Durham, NC, USA

    Date and Time: 29.11.2021 at 16:15 via Zoom

  • Good Scientific Practise for Doctoral Researchers

    Speaker: Dr. Peter Schröder, brain4hire, Graduiertenzentrum TU Dortmund

    Date and Time: 15.11.2021 at 16:15 via Zoom

  • Recent extensions on boosting for statistical modelling

    Speaker: Prof. Dr. Andreas Mayr, Head of WG Statistical Methods in Epidemiology, Universität Bonn

    Date and Time: 25.10.2021 at 16:15 via Zoom

  • Polygenic risk scores – Applicability beyond risk prediction and for different omics data

    Speaker: Anke Hüls, PhD, MSc, Assistant Professor, Department of Epidemiology and Gangarosa Department of Environmental Health, Emory University

    Date and Time: 05.08.2021 at 16:15 via Zoom

  • Advances in dose-response analysis

    Speaker: Prof. Christian Ritz , National Institute of Public Health,Copenhagen

    Date and Time: 28.06.2021 at 16:15 via Zoom