Missing data
Template:Missing data Editor-In-Chief: C. Michael Gibson, M.S., M.D. [1] Gonzalo Romero, M.D.[2]
Overview
Classification
Missing completely at random (MCAR)
It is independent of observed and non-observed data. Non-missing data constitutes effectively a random sample (example, a rater that becomes sick or loss of study files)
Missing at random (MAR)
Probability of a value being missing will generally depend on observed values (NOT MISSING VALUES), so it does not correspond to the intuitive notion of 'random'.
Old subjects might drop out a treatment because they have walking difficulties – as they cannot go to the clinic center – however among older subjects, the likelihood of dropping out does not relate to the outcome.
Missing not at random (MNAR)
Present when the pattern of missing data are related to unobserved data - therefore it is impossible to predict data from other values from the dataset