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mice package

2 messages · Giuseppe Biondi Zoccai, Bert Gunter

#
I am using the mice package to impute some missing values, and it work
nicely.
I am facing a tricky strategic question though.
Basically, I am working on predictors of myocardial infarction, with all
patients having baseline features (eg age, gender), despite a few missing
values.
Some patients have performed also a stress test, with specific continous
details (eg stress duration), but others haven't.
What should I do to capture the information associated with stress test
features?
A complete case analysis will of course exclude all those without a stress
test (roughly 50%).
Is it reasonable to impute with mice the stress features among also those
who did not undergo any stress test?
Or should I best create a factor variable such as stress_status (0- no
stress, 1-stress with low tolerance, 2-stress with high tolerance, and so
forth)?
Thanks for the help
Giuseppe
#
While your queries certainly intersect R , they are mostly about
statistical methodology for this special kind of missing data. This list is
mostly about R programming. I think you would do better posting to a
statistics list, like stats.stackexchange.com . Advice there might bring
you back here to ask about R implementation, but that's not your current
concern.

Cheers,

Bert

On Monday, March 7, 2016, Giuseppe Biondi Zoccai <gbiondizoccai at gmail.com>
wrote: