The phrase "attrition is dependent" is correct and usable in written English.
You can use it when discussing factors that influence attrition rates in various contexts, such as business, education, or research.
Example: "In our study, we found that attrition is dependent on several key variables, including participant engagement and support systems."
Alternatives: "attrition relies on" or "attrition is influenced by".
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.
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.
This further implies that attrition is dependent on variables with missing data because the researcher generally only has information on follow-up variables from those who stayed in the study.
In real life settings, the researcher will not know for sure whether or not RTM is supposed to happen or not, or the degree to which attrition is dependent on baseline versus follow-up variables.
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.
It may be that the ways different factors affect attrition are dependent on whether the original sample was drawn from a high-risk population.
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.
