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. Author manuscript; available in PMC: 2009 Jun 12.
Published in final edited form as: J Am Geriatr Soc. 2009 Feb 10;57(4):722–729. doi: 10.1111/j.1532-5415.2008.02168.x

Table 3.

Mechanisms of Missing Data in Clinical Research

Mechanism Definition Example Prevalence
Missing Completely at Random (MCAR) If the likelihood of being missing is not related to either the value of the missing variable or to the values of any other variables in the data set. A set of samples are “lost” due to lab error or an instrument is wrongly calibrated for a day on which random sample of subjects were measured. Almost never occurs.
Missing at Random (MAR) If the likelihood of missing data can be completely explained by other variables in the analysis. The probability of missing data on ADL can be explained by cognition, comorbidity, and living arrangements. Other data can sometimes provide a good prediction, but missingness is rarely completely explained.
Missing Not at Random (MNAR) If missing values are not randomly distributed across participants, and the probability of being missing cannot be predicted from the other variables. The probability of missing data on CESD is related to cognitive status, which was never measured. Most missing data.