Wir organisieren regelmäßige Vorträge und Veranstaltungen, um eine stetige wissenschaftliche Weiterbildung zu unterstützen. Diese Veranstaltungen sind öffentlich. Wir freuen uns auf Ihr Kommen.

Gerne können Sie sich auch in unseren Verteiler eintragen lassen und werden dann direkt über neue Termine informiert. Bitte übersenden Sie Ihre Anmeldung per E-Mail an unser IMBE Sekretariat (Frau Maryam Big ).

Kolloquium Epidemiologie und Biostatistik

Im Rahmen unseres Kolloquiums Epidemiologie und Biostatistik laden wir regelmäßig herausragende externe Wissenschaftler:innen aus den Bereichen Epidemiologie und Biometrie zu uns ein.

Die nächsten Vorträge sind derzeit in Planung.

Seminarreihe Epidemiologie und Biostatistik

Die Seminarreihe Epidemiologie und Biostatistik ist eine fortlaufende Veranstaltung. Regelmäßig laden wir herausragende externe Wissenschaftler:innen aus den Bereichen Epidemiologie und Biometrie zu uns ein.

Use of routine data for clinical research – Part I: Data Sources

Donnerstag, 4. Juli 2024 | 16:00 bis 17:00 h | N55 SR 310/311

Dr. Layla Riemann - Institut für Angewandte Medizininformatik, UKE
Dr. Jan Gewehr - Forschungs-IT, UKE

Sources of routine data I: from patient records to the UKE Data Hotel

Donnerstag, 11. Juli 2024 | 16:00 bis 17:00 h | N45 SR 4

Prof. Dr. Enno Swart - Institut für Sozialmedizin und Gesundheitssystemforschung, Otto von Guericke Universität Magdeburg

Sources of routine data II: from insurance claims to analysable datasets

Patient data documented for patient care and administration is becoming increasingly accessible to researchers. In our seminar series, we explore the challenges and opportunities this routine data holds. In this first part, we will answer what information can be made accessible for research use and which strengths and pitfalls have to be expected in its use.

Gastgeber: Prof. Dr. Malte Kohns Vasconcelos
Institut für Medizinische Biometrie und Epidemiologie, Universitätsklinikum Hamburg-Eppendorf

Frühere Veranstaltungen

  • Dr. Tamara Schamberger
    Data Science Group, Faculty of Business Administration and Economics, Bielefeld University

    Structural Equation Modeling with Composites

    Structural equation modeling (SEM) is a widely applied method in various disciplines, including business, economics, psychology, sociology, and medicine. Its popularity is arguably due to its ability to model and assess theories comprising theoretical concepts and to account for various forms of measurement errors.

    Traditionally, SEM has been primarily used to model theoretical concepts as latent variables, i.e., variables that explain the variance-covariance structure of their related variables. In recent years, the composite, i.e., a linear combination of more elementary variables that transmits all information between the variables that make up the composite and all other variables in the model, has gained increasing attention as a second type of theoretical construct in SEM. However, due to the types of relations in a composite model, the integration of composites in a structural equation model is not straightforward.

    This presentation will introduce the composite model and discuss various model specifications for incorporating composites into a structural equation model. Finally, the presentation will discuss the main approaches to estimating composite models in SEM.


  • Dr. Max Westphal
    Fraunhofer-Institut für Digitale Medizin, Bremen

    Improving model and algorithm evaluation in predictive modelling (one step at a time)

    In applied predictive modelling, practitioners usually face many methodological questions which are related to different objectives of the overall development and evaluation pipeline, e.g.:

    “How should I split my data for training, comparison and testing purposes?”
    “Which metric is suitable for assessing the utility of the developed models?”
    “How can I quantify uncertainty of my evaluation results?”

    While “standard” answers to these questions exist, it is rarely clear if and in what sense they are optimal or even admissible for the prediction task at hand. In the first part of the talk, we will consider three relevant and concrete examples of such questions related to model evaluation. Furthermore, we will illustrate how the derivation of more appropriate (than “standard”) answers is in principle possible based on recent methodological work.

    However, finding problem-tailored solutions in the scientific literature can also be difficult and time-consuming, in particular for practitioners without a specific data science background. In the second part of the talk, I will therefore introduce the expert system mlguide which is currently under active development at Fraunhofer MEVIS. mlguide aims to provide interactive support to practitioners for solving methodological questions in applied machine learning problems, such as those mentioned before. I will illustrate the overall system architecture, some insights into the guidance engine, and current limitations. To conclude, I will give an outlook on planned features and user research.


  • 14.04.2023 16 Uhr
    W30 Hörsaal
    Einführung in die Survivalanalyse * Dr. Anika Buchholz,
    Prof. Dr. Karl Wegscheider
    19.04.2023 16:30 Uhr
    N55 SR 310/11
    Competing Risks Sandra Schmeller, M.Sc.
    26.04.2023 16 Uhr
    N55 SR 210/11
    Independent, Random or Causal Censoring: examples Prof. Dr. Jan Beyersmann
    03.05.2023 16 Uhr
    O45 Hörsaal
    Causal mediation analysis of time-to-event endpoints Prof. Dr. Stijn Vansteelandt
    10.05.2023 16 Uhr
    N55 SR 210/11
    Multi-state Models for Recurrent Events Prof. Dr. Per Kragh Andersen
    30.05.2023 16:30 Uhr
    N30 Hörsaal
    Case-control and sampling designs for the time-to-event analyses Dr. Jan Feifel
    14.06.2023 16:30 Uhr
    W40 Hörsaal
    Estimands for recurrent event endpoints Mouna Akacha, PhD
    19.06.2023 16 Uhr
    N55 SR 210/11
    Missing disease information due to death in time-to-event analyses Dr. Nadine Binder
    29.06.2023 16 Uhr
    Introduction to Maximum-Accuracy Survival Analysis Paul Yarnold, PhD
    06.07.2023 16 Uhr
    N55 SR 310/11
    Inverse Probability of Censoring Weighting in Machine Learning* Prof. Dr. Antje Jahn
    13.07.2023 16 Uhr
    Two-stage adaptive design for prognostic biomarker signatures with a survival endpoint Biyue Dai, PhD
    Gastgeberin: Prof. Dr. Antonia Zapf
    Institut für Medizinische Biometrie und Epidemiologie, Universitätsklinikum Hamburg-Eppendorf

    * Vortrag auf Deutsch