Table 1.

Missing-data taxonomy applied to missing outcomes

	Missing-data mechanism
	Ignorable	Non-ignorable
Definition	Likelihood-based inference for the outcome model does not involve parameters modeling the missing-data mechanism*	Not ignorable
Implication	Missing in outcome depends only observed variables	Missing in outcome depends on at least one unobserved variable
Outcome is said to be	Missing at random (MAR)	Missing not at random (MNAR)
Special cases	Missing completely at random (MCAR) Missing in outcome occurs with constant probability	Directly non-ignorable Missing in outcome depends only on outcome Indirectly non-ignorable Missing in outcome does not depend on outcome and depends on other partially missing variables

^*The sets of parameters for the outcome model and missing-data mechanism do not overlap and do not constrain one other