A Bootstrap Method To Test If Study Dropouts Are Missing Randomly

Withdrawing from a longitudinal investigation is a common problem in epidemiological research. This paper describes a nonparametric method, based on a bootstrap approach, for assessing whether dropouts are missed at random. The basic idea is to compare scores of dropouts and non-dropouts at different assessments using a weighted nonparametric test statistic.A Monte Carlo investigation evaluates the comparative power of the test to violations from populations normality, using three commonly occurring distributions. The test proposed here is more powerful than the parametric counterpart under distributions with extreme skews.The method is applied to a longitudinal community-based study investigating mental disorders. It is found that dropouts did not differ from the other subjects with respect to two psychological variables, although chi-square tests gave some other impressions.