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Veranstaltung

17.08.2020 bis 21.08.2020

Rolf H.H. Groenwold / Maarten van Smeden: "Causal research and prediction modeling"

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Ausschließlich für Fachpublikum

Intensive Short Course, Aug 17 - 21, 2020

Flyer Causal Research and prediction modeling, Aug 17 - 21, 2020

The intent of this intensive short course is to strengthen the methodological skills of the research community. Upon successful completion of the course, participants will have a deeper understanding of methods in causal and prediction research and increased confidence in how to apply these tools in their everyday research practice.

Learning objectives:

By the end of this week, participants should be able to:

  • Critically assess the results of epidemiological studies on causal relationships or prediction models
  • Correctly define exposures and learn how to best represent them in models
  • Understand difference between various sources of bias (confounding, measurement error and missing data) and the way these biases may differentially affect studies on causal relationships and prediction models.
  • Describe key assumptions of methods used to control for (time-varying) confounding.
  • Describe key assumptions of methods used to handle missing observations.
  • Understand the reasons for and consequences of overfitting prediction models
  • Describe recent developments in the fields of causal research and prediction modelling

Prerequisites:

  • Basic knowledge of epidemiology
  • Familiarity with R statistical software (for a short introduction see http://www.r-tutorial.nl/)

Fees:

  • 750 €
  • 510 € for enrolled students (proof required)

3 ECTS

Registration:
Please use the registration form or send an e-mail to Tanja Te Gude
Tel. +49 30 450 570 812
Email

Organisatorisches

Referenten:

Rolf H.H. Groenwold, MD, PhD, Leiden University Medical Center (NL)
Maarten van Smeden, PhD, Leiden University Medical Center (NL)

Veranstalter:

Berlin School of Public Health
Institute of Public Health

Zeit:

9:00 - 17:00 Uhr

Ort:

Charité Campus Virchow

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