We extend current knowledge by examining the effect of attrition under conditions with different levels of dependency between risk of attrition and predictors as well as follow-up variables.
Analyses were then run on sub-samples with different attrition rates.
It is recommended to investigate the possible bias from missing data by comparing subsamples of patients with different attrition patterns using mixed-model statistics [ 40].
The mechanisms of attrition, abrasion and erosion act together, each with different intensity and duration to produce a multitude of different wear patterns.
Investigation and adjustment for attrition needs to consider any risk factors that may be associated with the outcome of interest in each particular study and how these risk factors might be associated with different types of attrition [ 2, 3].
We thus simulated situations with different combinations of dependency between attrition and both Baseline predictor and Follow-up outcome, including a situation with completely random attrition.
Although direct evidence for this is limited with some studies[ 5] finding evidence of biased estimators with different response rates, while others found the associates were unaffected by selective attrition[ 6- 8].
Maybe the opponents will wage a war of attrition -- suing and politicking and bickering -- that will stall the current plans, perhaps until another Mayor with different ideas and alliances is elected.
A total of 1200 participants were proposed for potential attrition, with 100 participants in each of 6 different types of cancers diagnosed for the case managed or control group.
No factors affected different types of attrition in different directions, which makes adjusting for attrition biases possible.
