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After Work Statistics

How was that again with case number planning? How to interpret an odds ratio? Which test for which data? Although every researching physician should have received a certain statistical education, the important statistical methods and topics are often no longer present once you want to apply them. What now?

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After Work Statistics

As a supplement to our individual statistical consultation service, this lecture series offers a refresher on frequently occuring statistical problems. Right after work! The lectures are explicitly aimed at medical researchers. There are eight different topics, each of which is held twice during a semester. A lecture of 30 minutes plus discussion and you are statistically "up to data" again!

Seven topics are taught in German from April 17 to June 12, 2023 (deutsche Seite) and in English from June 16 to July 31 (!), 2023.

Time and Location:

mondays from 4pm to 5pm via MS Teams. The link to the lecture (Englisch and German) will be published on the day of the individual event in section Topics and Material on this page.

Registry and ECTS:

You may

  • just show up for an individual lecture or
  • gain 0.8 ECTS for attending each and every lecture.
    • If you need to have your participation certified, please register by online form.
    • The online form will be activated until June 18, 2023, 11:59 am.
    • you may attend an individual lecture either in German or in English
    • Certificates of attendance will be issued after the last event, i.e. after the end of the lecture period SS 2023.

Topics and Material

For dates, presentations, as well as Shiny Apps in German, click here.





June 19, 2023

So many tests! The agony of choice.

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June 26, 2023

So many questions! Multiple testing.

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July 3, 2023

So many patients? Sample size calculation.

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July 10, 2023

What is it this odds ratio? Logistic regression.

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July 17, 2023

Missing information? Dealing with missing data.

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July 24, 2023 (!)

The right time? Survival analysis.

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July 31, 2023 (!)

Who The variety of influences - Mixed models.

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