Comment inline
On 19/12/2014 11:17, aoife doherty wrote:
Many thanks, I appreciate the response.
When I convert the missing values to NA and run the cox model as described
in previous post, the cox model seems to remove all of the rows with a
missing value (as the number of rows "n" in the cox output after I
completely remove any row with missing data is the same as the number of
rows "n" in the cox output after I change the missing values to NA).
What I had been hoping to do is not completely remove a row with missing
data for a co-variable, but rather somehow censor or estimate a value for
the missing value?
I think you are searching for some form of imputation here. A full
answer would be way beyond the scope of this list as it depends on so
many things including the mechanism driving the missingness.
Have a look at
http://missingdata.lshtm.ac.uk/
and see whether that helps.
In reality, I have ~600 people with survival data and say 6 variables
attached to them. After I incorporate a 7th variable (for which the
information isn't available for every individual), I have 400 people left.
Since I still have survival data and almost all of the information for the
other 200 people (the only thing missing is information about that 7th
variable), it seems a waste to remove all of the survival data for 200
people over one co-variate. So I was hoping instead of completely removing
the rows, to just somehow acknowledge that the data for this particular
co-variate is missing in the model but not completely remove the row? This
is more what I was hoping someone would know if it's possible to
incorporate into the model I described above?
Thanks
On Fri, Dec 19, 2014 at 10:21 AM, Ted Harding <ted.hard...@wlandres.net>
wrote:
Hi Aoife,
I think that if you simply replace each "*" in the data file
with "NA", then it should work ("NA" is usually interpreted
as "missing" for those functions for which missingness is
relevant). How you subsequently deal with records which have
missing values is another question (or many questions ... ).
So your data should look like:
V1 V2 V3 Survival Event
ann 13 WTHomo 4 1
ben 20 NA 5 1
tom 40 Variant 6 1
Hoping this helps,
Ted.
On 19-Dec-2014 10:12:00 aoife doherty wrote:
Hi all,
I have a data set like this:
Test.cox file:
V1 V2 V3 Survival Event
ann 13 WTHomo 4 1
ben 20 * 5 1
tom 40 Variant 6 1
where "*" indicates that I don't know what the value is for V3 for Ben.
I've set up a Cox model to run like this:
#!/usr/bin/Rscript
library(bdsmatrix)
library(kinship2)
library(survival)
library(coxme)
death.dat <- read.table("Test.cox",header=T)
deathdat.kmat <-2*with(death.dat,makekinship(famid,ID,faid,moid))
sink("Test.cox.R.Output")
Model <- coxme(Surv(Survival,Event)~ strata(factor(V1)) +
strata(factor(V2)) + factor(V3)) +
(1|ID),data=death.dat,varlist=deathdat.kmat)
Model
sink()
As you can see from the Test.cox file, I have a missing value "*". How
and
where do I tell the R script "treat * as a missing variable". If I can't
incorporate missing values into the model, I assume the alternative is to
remove all of the rows with missing data, which will greatly reduce my
data
set, as most rows have at least one missing variable.
Thanks
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E-Mail: (Ted Harding) <ted.hard...@wlandres.net>
Date: 19-Dec-2014 Time: 10:21:23
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