At the end of the course, the student will be able to:
Explain different mechanisms giving rise to missing data
Recognize missing data as a potential source of bias in epidemiologic research
Describe key assumptions of methods to handle missing data
Apply imputation methods to deal with missing data
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Education form |
Startdate |
Enddate |
Registration period |
Face-2-Face |
30-5-2022 |
4-6-2022 |
BMS_P3_A |
Contact details: Educational Office Epidemiology
E-mail: msc-epidemiology@umcutrecht.nl
Registration:
Register via https://www.msc-epidemiology.nl/single-courses.html
Course coordinator:
Dr. M. (Maarten) van Smeden
Course description:
Even in well designed and conducted epidemiological studies, data will be missing. This may include missing observations of the exposure and under study, confounders, or the outcome.
Possible mechanisms for data being missing will be discussed, as well as their potential impact in terms of bias. Focus will be on methods t handle missing data. Examples and exercises will come from various epidemiological studies, including diagnostic, prognostic, etiologic, and therapeutic studies.
Literature/study material used:
-
Mandatory for students in own Master’s programme:
MIght be for a specialization programme of Epidemiology & Epidemiology Postgraduate
Optional for students in other GSLS Master’s programme:
Yes
Prerequisite knowledge:
Introduction to Epidemiology
Classical Methods in Data Analysis
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