Seminar
| Datum und Uhrzeit | Details |
|---|---|
| 27.10.2025 at 4:15 p.m., TU Dortmund, CDI 120 | To be announced Prof. Isao Yokota, Associate Professor, Department of Biostatistics, Graduate School of Medicine, Hokkaido University, Japan |
| 20.10.2025 at 4:15 p.m., TU Dortmund, via Zoom | To be announced Dr. Giulia Baldrighi, Senior Researcher, Clinical Neurosciences, University of Turku, Finland |
| 21.07.2025 at 4:15 p.m., TU Dortmund, CDI 120 | Unveiling the Dynamics of Physical Activity and Mood in Schizophrenia Spectrum Disorders: A Bayesian Network Approach from the Multicentric DiAPAson Project M.Sc. Martina Cannavale, PhD student, University of Brescia, Italy |
| 07.07.2025 at 4:15 p.m., TU Dortmund, CDI 120 | Investigating the uncertainty caused by researchers' degrees of freedom in biostatistics, bioinformatics, and machine learningM.Sc. Milena Wünsch, PhD student, Institut für Medizinische Informationsverarbeitung Biometrie und Epidemiologie (IBE), LMU, Munich, Germany |
| 26.05.2025 at 4:15 p.m., TU Dortmund, CDI 120 | Using flowR to improve the work with R codeM.Sc. Florian Sihler, Research Assistant, Institute of Software Engineering and Programming Languages, Ulm University, Germany |
| 05.05.2025 at 5:00 p.m., TU Dortmund, via Zoom | Two-sample hypothesis testing for high-dimensional and non-Euclidean dataProf. Hao Chen, Associate Professor, Department of Statistics, University of California, Davis, USA |
| 28.04.2025 at 4:15 p.m., TU Dortmund, CDI 120 | Bayesian compositional modelling of water quality in Scottish lochsProf. Mark J. Brewer, Director Biomathematics & Statistics Scotland (BioSS), The James Hutton Institute, Aberdeen, Scotland, UK |
| 17.02.2025 at 4:15 p.m., TU Dortmund, CDI 120 | Probabilistic modelling of drug combination screens for precision oncologyProf. Manuela Zucknick, Department of Biostatistics, Faculty of Medicine, University of Oslo, NorwayWith high-throughput drug sensitivity screens we can quickly test compounds on cancer cell lines to determine treatment efficacy. Since molecular characterisation of the cell lines by various omics data sets is frequently available, we can link molecular features to treatment efficacy. The estimation of drug synergy is important when testing multiple compounds, but in vitro cell viability measurements can be imprecise due to measurement errors and limited number of data points, especially for drug combination experiments. We have earlier proposed a Bayesian model for drug response estimation with uncertainty quantification for an individual experiment, i.e. for one drug combination and one cell line. This model formed the basis for a probabilistic framework for dose–response prediction in high-throughput drug combination screens on large numbers of drugs and cell lines, which uses permutation invariant multi-output Gaussian processes. I will introduce the modelling framework and show how the model is able to learn from noisily observed measurements in settings where the underlying dose–response experiments are of varying quality, utilize different experimental designs, and the resulting training dataset is sparsely observed; and that it can accurately predict dose–response and capture relevant features indicating synergistic interaction between drugs. Joint work with Leiv Rønneberg and Paul Kirk. |
| 30.01.2025 at 4:15 p.m., TU Dortmund, M/E21 | Additive Density-on-Scalar RegressionProf. Sonja Greven, Chair of Statistics, School of Business and Economics, Humboldt-Universität zu Berlin, GermanySamples of distributions are of interest in many applications, from income distributions in economics to distributions of soil contaminants in different states in geochemistry. Densities are useful summaries to work with, as they can be defined for continuous, discrete, mixed or even multivariate distributions and intuitively show bimodalities and areas of high and low probability mass. We present structured additive regression models to model densities given scalar covariates. To preserve nonnegativity and integration to one, we formulate our models for densities in so-called Bayes Hilbert spaces, enabling us to not only consider continuous densities, but also, e.g., discrete densities (compositional data) or mixed densities. Mixed densities occur in our application motivated by research on gender identity norms and the distribution of the woman's share in a couple's total labor income, as the woman's income share is a continuous variable having discrete point masses at zero and one for single-earner couples. We discuss interpretation of effect functions in our model via odds-ratios. We consider two data situations: First, where whole densities are observed and are directly used as responses. Second, when only samples or even individual realizations of the conditional distributions are observed, we use our additive regression approach to model the conditional density given covariates, nicely linking our models to distributional regression models. We derive consistency and asymptotic normality of the proposed (penalized) maximum likelihood estimator. To facilitate estimation, we show approximate equivalence of (penalized) likelihood and estimator of the original conditional density model to those of a certain Poisson additive model, greatly simplifying our implementation in an R package. We apply our framework to the motivating gender economic data from the German Socio-Economic Panel Study (SOEP) to model the distribution of the woman's share in a couple's total labor income, given year, place of residence and age of the youngest child. |
| 27.01.2025 at 4:15 p.m., TU Dortmund, CDI 120 | Genetic prediction modeling based on large cohort studies via statistical boostingHannah Klinkhammer, M. Sc., PhD Student at Institut für Medizinische Biometrie, Informatik und Epidemiologie (IMBIE), Universität Bonn |
| 10.02.2025 at 4:15 p.m., TU Dortmund, CDI 120 | Optimizing Dose-Finding Studies for Drug Combinations Based on Exposure-Response ModelsAndrew Hooker, Ph.D., Professor of Pharmacometrics, Department of Pharmacy; Pharmacometrics, Uppsala University, Sweden |
| 24.06.2024 at 4:15 p.m., TU Dortmund, CDI 120 | (Joint) Modelling approaches for lung function decline and risk of death for cystic fibrosis patientsProf. Dr. Elisabeth Bergherr, Professur für Raumbezogene Datenanalyse und Statistische Lernverfahren, Georg-August-Universität Göttingen |
| 22.04.2024 at 4:15 p.m., TU Dortmund, CDI 120 | Multiple Endpoints and Prioritized Outcomes: Effects and Nonparametric Analysis MethodsProf. Dr. Edgar Brunner, Emeritus, Department of Medical Statistics, Universitätsmedizin GöttingenThe analysis of multiple and composite endpoints in medical and biological trials is discussed since many years. Numerous procedures have been developed for different situations and statistical models. Composite endpoints are suggested to combine several outcomes into one single endpoint which can be evaluated by a simple analysis. The danger, however, when using a combined endpoint is that the failure of an effect in an important clinical outcome may be masked by a large effect of a less important clinical outcome. Therefore, it has been suggested to prioritize the outcomes and if the outcome of a patient in the treatment group is better than that of a patient in the control group then score 1 is assigned or score 0 if it is worse. In case of no difference then one steps down to the next important outcome until no difference is found in the last important endpoint resulting in score ½. This handling of prioritized outcomes has been suggested by Buyse (2010) who called this procedure ‘generalized pairwise comparisons’ (GPC) since all pairs of patients from the treatment and the control group are compared by prioritizing the components. The resulting statistical procedure is closely related to the Mann-Whitney statistic. To understand the advantages, drawbacks, and limitations of the GPC, the relations to rank procedures and U-statistics are presented and discussed. The practical use and the interpretation are demonstrated my means of examples. Buyse M. Generalized pairwise comparisons of prioritized outcomes in the two-sample problem. StatMed. 2010; 3245- 3257. |
| 26.02.2024 at 4:15 p.m., TU Dortmund, CDI 120 | Allelic expression imbalance to uncover disease-relevant gene regulationAssociate Professsor Michael I. Love, PhD, UNC School of Medicine Genetics, Chapel Hill, USAGenome-wide association studies (GWAS) have identified tens of thousands of genomic loci that are associated with complex traits or diseases, many of which are located in non-coding regulatory regions. One potential mechanism by which allelic variation in these non-coding regions may affect phenotypes is that the variants may influence transcription in cis. A non-coding region that affects local transcription may be referred to as a cis-regulatory element (CRE). Individuals that are heterozygous at variants in CRE may exhibit imbalanced allelic expression at the regulated genes. With RNA-sequencing (RNA-seq) experiments, it is possible to observe such imbalance in allelic expression in the sequenced reads for those individuals also heterozygous for a variant in the exons of a regulated gene. In this talk I will give an overview of methods for detecting AEI from existing short-read RNA-seq data, and how new technologies will help in this task. |
| 12.02.2024 at 4:15 p.m., TU Dortmund, CDI 120 | Sample size planning for multiple contrast testAnna Pöhlmann, Institut für Biometrie und Klinische Epidemiologie, Charité Berlin |
| 22.01.2024 at 4:15 p.m., TU Dortmund, via Zoom | Phases of development for statistical methodsDr. Tim Morris, Principal Research Fellow MRC Clinical Trials Unit at UCLApplied researchers need to be able to make informed decisions about which methods to use when. Research into statistical methods should thus provide an evidence-base that facilitates this. Unfortunately, the appetite of funders and journal editors for novelty and innovation in statistical methodology means the evidence base is often sparse. How should a method be developed and evaluated to go from ‘neat idea’ to trustworthy? We should be able to say when to use and avoid it, and how to make decisions for the study at hand. By analogy to the familiar phases of drug-development, I will outline four ‘phases’ of research for statistical methods. This framework helps us consider the contribution of a piece of statistical research to our overall understanding. The aims of this view are to: 1. Help frustrated methodological researchers understand why their method has not been universally adopted; 2. Legitimise ‘late-phase’ statistical methods research; 3. Give applied researchers a way to articulate their hesitation about new methods. I will give a few examples to illustrate, note some difficulties, and leave time for discussion. |
| 15.01.2024 at 4:15 p.m., TU Dortmund, CDI 120 | Methods for the meta-analysis of ROC curves – A statistical challengeProf. Dr. Annika Hoyer, Leitung Biostatistik und Medizinische Biometrie Universität Bielefeld |
| 08.01.2024 at 4:15 p.m., TU Dortmund, via Zoom | Insights into the world of a CMC statisticianDr. Beate Presser, Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, BiberachThis presentation provides insights into the life of a CMC (Chemistry, Manufacturing, and Controls) statistician in the pharmaceutical industry, elucidating statistical questions and their solutions through various examples. These examples range from the concrete implementation of an ICH Q14 guideline on the development of analytical methods with DoE (design of experiment) to examples from the field of process modeling and unplanned data. Different model types are explained in practical examples. |
| 30.10.2023 at 4:15 p.m., TU Dortmund, CDI 120 | Statistical Planning and Analysis of Translational TrialsFrank Konietschke, Kommissarische Institutsleitung, AG-Leitung Statistische Methoden der Translation und Früher Klinischer Studien, Charité Berlin |
| 23.10.2023 at 4:15 p.m., TU Dortmund, CDI 120 | How to better detect differentially expressed proteins when the sample size is smallKai Kammers, Assistant Professor of Oncology, Johns Hopkins Medicine |
| 17.07.2023 at 4:15 p.m., TU Dortmund, CDI 120 | Sparse group models for leveraging pleiotropy effects from GWASBenoit Liquet-Weiland, Professor of Mathematical and Computational Statistics, School of Mathematical and Physical Sciences, Macquarie University |
| 26.06.2023 at 4:15 p.m., TU Dortmund, via Zoom | Talk 1: Use of Environmental Mixture Methodology in Epidemiology Studies Grace Christensen und Junyu Chen, MPH PhD Candidate Emory University’s Rollins School of Public Health |
| 12.06.2023 at 4:15 p.m., TU Dortmund, via Zoom | Talk 1: EFSA’s guidance on the use of the BMD approach in risk assessment Prof. Dr. Marc Aerts, Hasselt University Belgium, und Dr. José Cortiñas Abrahantes, EFSA European Food Safety Authority |
| 22.05.2023 at 4:15 p.m., TU Dortmund, CDI 120 | Efficient Designs for toxicological experimentsWeng Kee Wong, Professor at the Department of Biostatistics, University of California, Los AngelesI first discuss the importance and challenges in toxicology problems, which are increasingly pervasive in our daily lives. A main focus of the talk is how to implement more informative designs in the laboratory that will likely lead to more accurate statistical inference. Optimal experimental design theory and ideas will be used to construct model-based optimal designs for a variety of common nonlinear models used in toxicology and I will present efficient designs to estimate model parameters, or, one or more functions of the model parameters, including optimal designs to identify thresholds, detect presence of hormesis, and in identifying an adequate model among several postulated models. In addition, I will discuss metaheuristics as general optimization tools to find optimal designs for more complex problems and demonstrate how to them in web-based tools to find optimal designs. If time permits, I will also discuss one of my current work in toxicology that concerns Haber’s law. |
| 18.04.2023 at 4:15 p.m., TU Dortmund, ME21 | How does air pollution effect our brain? The complex relationship between environmental, social and epigenetic factors?Anke Huels, Assistant Professor, Department of Epidemiology and Gangarosa Departmentof Environmental Health, Rollins School of Public Health, Emory University, Atlanta, USA |
| 03.04.2023 , 4:15 p.m., TU Dortmund, CDI 120 | Prediction Intervals for overdispersed Poisson dataDr. Max Menssen, Institut für Zellbiologie und Biophysik, Leibniz Universität Hannover |
| 23.01.2023 at 4:15 p.m., TU Dortmund, via Zoom | Novel clinical trial designs for precision oncology:Prof. Dr. Annette Kopp-Schneider, Deutsches Krebsforschungszentrum in der Helmholtz-Gemeinschaft, HeidelbergThe aim of precision oncology is to provide a targeted therapy for each individual tumor. To reach this goal, it is necessary to identify in each patient actionable biomarkers and to determine a matched targeted therapy for the specific biomarker(s). Clinical trials are necessary to investigate whether the biomarker-treatment combination in fact has the desired effect. Due to the large number of possible biomarker-treatment combinations, and due to the small patient numbers matching such a combination, classical clinical phase III trials are not feasible in this situation. Precision oncology therefore requires the development of novel trials designs. We will discuss the concepts of basket, umbrella and platform designs for precision oncology and will present the challenges for the statistical analysis in these trial designs. Bayesian methods are naturally suited for precision oncology clinical trials as they allow for more flexible designs and they are suited for incorporation of external information and for borrowing of information between arms of clinical trials. We will discuss the use of Bayesian methods for clinical trials and present some current developments. |
| 16.01.2023 at 4:15 p.m., TU Dortmund, CDI 120 | Optimal test procedures for multiple hypotheses controlling the familywise expected lossProf. Dr. Frank Bretz, Novartis Pharma AG, Biostatistics & Statistical Reporting, CH-4002 Basel, Switzerland |
| 05.12.2022 at 4:15 p.m., TU Dortmund, CDI 120 | Genotoxicity Assessments of PharmaceuticalsProf. Dr. Helena Geys, Hasselt University, Belgium |
| 28.11.2022 at 4:15 p.m., TU Dortmund, CDI 120 | A novel group-sequential phase II design for clinical trials with binary endpoints based on Bayesian evidence valuesDr. Riko Kelter, Department of Mathematics, Research Group Stochastics, University of Siegen |
| 14.11.2022 at 4:15 p.m., via Zoom | A replication crisis in methodological statistical research?Prof. Dr. Anne-Laure Boulesteix, Institut für Medizinische Informationsverarbeitung Biometrie und Epidemiologie (IBE), Fakultät der LMU München |
| 07.11.2022 at 4:15 p.m., TU Dortmund, CDI 120 | Shinybrms and projpred: Introductory and advanced Bayesian (and not-so-Bayesian) statistics in practiceFrank Weber, Institut für Biostatistik und Informatik in Medizin und Alternsforschung (IBIMA) |
| 17.10.2022 at 4:15 p.m., TU Dortmund, CDI 120 | Virtual Control Groups: Using historical toxicity data to replace control-group animalsAlexander Gurjanov, PHD Student at Bayer Pharmaceuticals |
| 23.09.2022 at 9:30 a.m., TU Dortmund, CDI 116 and via Zoom | Talk 1: An Intro to ToxicR and Bayesian Dose-Response Matt Wheeler, Ph.D. National Institute of Environmental Health Sciences |
| 29.08.2022 at 4:15 p.m., via Zoom | The Hazelwood Health study and other opportunities in AustraliaProf. Michael Abramson, School of Public Health & Preventive Medicine, Monash University, Australia |
| 04.07.2022 at 4:15 p.m., TU Dortmund, M/E 21 and via Zoom | Random forests on high-dimensional data: from classification and survival analysis to generative modellingProf. Dr. Marvin N. Wright, Leibniz-Institut für Präventionsforschung und Epidemiologie – BIPS, Bremen |
| 27.06.2022 at 4:15 p.m., TU Dortmund, M/E 21 and via Zoom | Issues in supervised prediction and classification for complex chemical substancesProf. Fred Wright, Ph.D., Departments of Statistics and Biological Sciences, North Carolina State University, USA |
| 24.06.2022 at 12:00 p.m., TU Dortmund, M/E 21 and via Zoom | Modeling Heterogeneity for Stratified PopulationsProf. Menggang Yu, Department of Biostatistics and Medical informatics, University of Wisconsin-Madison, USA |
| 20.06.2022 at 4:15 p.m., via Zoom | Propensity Score: an Alternative Method of Analyzing Intervention EffectProf. Dr. sc. hum. Oliver Kuß, Deutsches Diabetes-Zentrum, Düsseldorf |
| 30.05.2022 at 4:15 p.m., via Zoom | Gene expression to Omics: The evolution of genetic researchAshtyn Areal, IUF Düsseldorf |
| 23.05.2022 at 4:15 p.m., via Zoom | Analysis and design of clinical trials with biologics using dose-time-response modelsMarkus Lange, Novartis |
| 16.05.2022 at 4:15 p.m., via Zoom | The case time series design for high-dimensional data analysesProf. Antonio Gasparrini, London School of Hygiene and Tropical Medicine, London, UK |
| 09.05.2022 at 4:15 p.m., via Zoom | Mehrstadien-Modelle in der Epidemiologie chronischen ErkrankungenProf. Dr. rer. nat. Ralph Brinks , Lehrstuhl für Medizinische Biometrie und Epidemiologie, Private Universität Witten/Herdecke gGmbH |
| 14.02.2022 at 4:15 p.m., via Zoom | Assessing interactive effects of air pollution and temperature– approaches and challenges?Dr. Susanne Breitner , Helmholtz-Zentrum München |
| 24.01.2022 at 4:15 p.m., via Zoom | Dose- response analysis in toxicology: considering dose both as qualitative factor and quantitative covariate (using R)Prof. Dr. Ludwig Hothorn, Biostatistician, Retired from Leibniz University Hannover |
| 10.01.2022 at 4:15 p.m., via Zoom | Systems genetics strategies for describing the transcriptional connectome and it's role in complex traitsAssociate Professor Laura Saba, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado, USA |
| 06.12.2021 at 4:15 p.m., via Zoom | Can statistics save preclinical research?Prof. Dr. med. Ulrich Dirnagl, Abteilungsdirektor Experimentelle Neurologie, Charité Berlin |
| 29.11.2021 at 4:15 p.m., via Zoom | Dose-response analysis for gene-expression dataScott S. Auerbach, PhD, und Matt Wheeler, PHD, Institute of Environmental Health Sciences, Durham, NC, USAAbstract |
| 15.11.2021 at 4:15 p.m., via Zoom | Good Scientific Practise for Doctoral ResearchersDr. Peter Schröder, brain4hire, Graduiertenzentrum TU Dortmund |
| 25.10.2021 at 4:15 p.m., via Zoom | Recent extensions on boosting for statistical modellingProf. Dr. Andreas Mayr, Head of WG Statistical Methods in Epidemiology, Universität Bonn |
| 05.08.2021 at 4:15 p.m., via Zoom | Polygenic risk scores – Applicability beyond risk prediction and for different omics dataAnke Hüls, PhD, MSc, Assistant Professor, Department of Epidemiology and Gangarosa Department of Environmental Health, Emory University |
| 28.06.2021 at 4:15 p.m., via Zoom | Advances in dose-response analysisProf. Christian Ritz , National Institute of Public Health,Copenhagen |