Dear Statalisters,
I'm trying to model data on change in
certain hormones over a period of time. The data on some of these have
a number of values that are set (currently) at a non-zero minimum
detectable value (MDL). Currently, I'm using a GLM with a gamma family
along with robust estimation of SE to model this data. The model works
reasonably fine on the data but I would like to improve this by
properly accounting for the information on the non-detectables.
I'm interested in the change over time of these hormones and so change
from the MDL to a higher value is useful information. Apart from the
GLM, I've looked at the tobit models and zero-inflated models. From
what I understand, the tobit model assumes that the data that is not
censored follows a normal distribution and this not the case in my
data. Zero-inflated models seem to be used for count rather than
continuous data.
Does anybody in the list have experience on modeling continuous data
with zero-inflation or data with a MDL issue?
Thank you in advance for any suggestions or comments,
Leny
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