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 to handle missing data. Examples and exercises will come from various epidemiological studies, including diagnostic, prognostic, etiologic, and therapeutic studies.