[R] Multilevel Survival Analysis - Cox PH Model

[dunner]

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.

I'd be appreciative of any help at all?

Thanks again,

R


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