On Jul 1, 2011, at 10:10 AM, dunner wrote:
Hello all, thanks for your time and patience.
I'm looking for a method in R to analyse the following data:
Time to waking after anaesthetic for medical procedures repeated on
the same
individual.
str(mysurv)
labelled [1:740, 1:2] 20 20 15 20 30+ 40+ 50 30 15 10 ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr [1:2] "time" "status"
- attr(*, "type")= chr "right"
- attr(*, "units")= chr "Day"
- attr(*, "time.label")= chr "ORIENTATION"
- attr(*, "event.label")= chr "FullyOrientated"
mysurv is constructed from the following data:
head(data.frame(MRN, ORIENTATION, FullyOrientated))
MRN ORIENTATION FullyOrientated
1 0008291 20 2
2 0008469 20 2
3 0008469 15 2
4 0010188 20 2
5 0013664 30 1
6 0014217 40 1
I had planned to use a Cox PH model to analyse time to waking
(ORIENTATION =
10, 15, 20 mins ....... 50 mins) and whether or not people (MRN) are
fully
awake within an hour (FullyOrientated). I've put GENDER, etc. into
the
model but I have the following bias:
The procedure is repeated weekly on each individual (MRN), so each
individual has 5-9 cases associated with them. Currently I am
including
these in the model as if they were independent.
Is there a way to account for the non-independence of these waking
times?
I'm thinking of something similar to the NLMER package and
Multilevel /
Mixed Effects analysis as described in Pinheiro and Bates.
Have you looked at the coxme package?
--
David Winsemius, MD
West Hartford, CT
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