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For polypropylene, attrition was dependent on the number of rotations of the valve, but for PVC there was also a further rate dependent effect.
The more strongly the attrition was dependent on baseline and follow-up variables, the more biased the results became.
Estimates of associations between variables became biased only when attrition was dependent on both baseline and follow-up variables.
When attrition was dependent on both baseline predictor and follow-up outcome to a moderate degree (b = .30 for both), results were more clearly biased.
When attrition was dependent upon the follow-up outcome to a moderate degree (b = .30) and in addition weakly dependent on the baseline predictor (b = .10), results were slightly biased.
They concluded that correlation estimates were biased when attrition differed between the control group and the intervention group, but that correlation estimates based on complete cases were unbiased when attrition was the same in both groups, even though attrition was dependent on measured and unmeasured variables.
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Scenarios where attrition is dependent on unobserved variables can be simulated by generating data sets where attrition is dependent on variables with missing values.
However, when attrition is dependent on both baseline and follow-up variables, regression estimates tend to be biased.
It may be that the ways different factors affect attrition are dependent on whether the original sample was drawn from a high-risk population.
The current findings suggest that such techniques are needed for MR analyses of the prediction of change when attrition is dependent on both baseline and follow-up variables.
Those who stay can be more different from those who drop out of a study at the time of follow-up than at baseline, suggesting that attrition is dependent on follow-up variables.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com