Missing not at random (MNAR)-Data following dropout are MNAR if the future statistical behaviour of a participant’s outcomes, given his/her past outcomes and covariates, would not have been the same whether he/she had dropped out or not. However, after this information has been taken into account (using baseline quality of life as a covariate in a regression model), no systematic differences exist between dropouts and completers. For example, missing post-baseline quality of life data may occur in patients with low baseline quality of life. Missing at random (MAR)-Data following dropout are MAR if the future statistical behaviour of a participant’s outcomes, given his or her past outcomes and covariates, would have been the same whether he/she had dropped out or not. For example, if a staff member forgets to administer questionnaires, then these missing data are likely to be MCAR. As a result, no systematic differences exist between dropouts and completers. Missing completely at random (MCAR)-Outcome data are MCAR if their missingness does not depend on observed covariates or previous outcomes.
#AVERAGE PERCENTAGE TOTAL COLLEGE DROP OUT TRIAL#
We begin with an example from a real randomised controlled trial and then show the generalisability of our assertions with a computer simulation study (see glossary). Our aims were to show that for continuous outcome data biased estimates of treatment effects can be obtained when no differential dropout occurs (refute myth 1) unbiased estimates of treatment effects can be obtained when differential dropout occurs (refute myth 2) and when missingness is non-random, the degree of bias depends, in part, on the actual mechanism of dropout. Whether dropout rates between the arms are differential or not can be a red herring the two key factors are the type of missingness and the statistical analysis. The inverse is true as well: unequal dropout rates do not mean the results are biased (myth 2).
Myth 2-if dropout rates are dissimilar between study arms, the results will necessarily be biased.Īlthough differential dropout can bias results, equal dropout rates between study arms does not imply that results will be unbiased (myth 1).
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