Missing Data

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.

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

Our courses are aimed at clinical researchers, nurses, general practitioners, and other health professionals who want to improve their skills in epidemiology, statistics and (clinical) research.

1 week, fulltime (face to face)