File:Fig1 Beaulieu-JonesJMIRMedInfo2018 6-1.png

Figure 1. Two general paradigms are commonly used to describe missing data. Missing data are considered ignorable if the probability of observing a variable has no relation to the value of the observed variable and are considered nonignorable otherwise. The second paradigm divides missingness into three categories: missing completely at random (MCAR: the probability of observing a variable is not dependent on its value or other observed values), missing at random (MAR: the probability of observing a variable is not dependent on its own value after conditioning on other observed variables), and missing not at random (MNAR: the probability of observing a variable is dependent on its value, even after conditioning on other observed variables). The x-axis indicates the extent to which a given value being observed depends on other values of other observed variables. The y-axis indicates the extent to which a given value being observed depends on its own value.

Beaulieu-Jones, B.K.; Lavage, D.R.; Snyder, J.W.; Moore, J.H.; Pendergrass, S.A.; Bauer, C.R. (2018). "Characterizing and managing missing structured data in electronic health records: Data analysis". JMIR Medical Informatics 6 (1): e11. doi:10.2196/medinform.8960. 

2018

Beaulieu-Jones, B.K.; Lavage, D.R.; Snyder, J.W.; Moore, J.H.; Pendergrass, S.A.; Bauer, C.R.

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