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The stronger the dependency between attrition and study variables, the more biased were the results.
The results from the simulation study showed that mean estimates became substantially biased even at relatively weak dependencies between follow-up variables and attrition, whereas estimates of associations between variables were more robust to dependencies between attrition and study variables.
Similar(58)
They did not compare different degrees of dependency between attrition and the study variables.
This study addresses the relation between attrition and characteristics of the study protocol, specifically contact frequency, and respondent burden.
We did not correct for attrition and study duration.
This study supported previous findings of associations between attrition and purging subtype.
As in our primary analysis, most studies did not find a relationship between attrition and gender, age or diabetes duration [ 24].
The interplay between attrition and abrasion has been demonstrated with casts from longitudinal growth studies of the same individuals of pre-contemporary aboriginal populations [ 10].
Previous studies have not found any consistent association between attrition and social class.
The empirical findings from these studies provided limited explanations of the relationship between attrition and other e-health factors.
The simulation study showed that estimates of means became biased even at low attrition rates and only weak dependency between attrition and follow-up variables.
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