Missing data and imputation

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Three mechanisms (assumptions) of data missing

Mechanisms (assumptions) of data missing

	Description	[*Example] A cohort study in which participants report their body weight every week to research center
Missing not at random MNAR	Missing depends on observed data themselves
Missing at randam MAR	Missing does not depend on observed data but may depend on unobserved data Missing can be explained	Female participants less likely report their body weights than male participants: Data of female participants are more likely missed not because of data (body weights) themselves but because female tend to be reluctant to report their body weights In other words, the variable of body weight itself does not affect the missing but another variable sex affects the missing of values in the variable body weight
Missing completely at random MCAR	Missing does not depend on observed data nor on unobserved data Missing occurs purely at random independely from any observed and unobserved data	A participant moves to outside cohort area because of family affair which has nothing to do with the study and is lost to follow up: Data after the move are missed not because of data (body weights) themselves nor because of the participant's unobserved (unmeasured) characteristic