Hello,
I am currently trying to simulate data and analyze it using the frailty option
in the coxph function. I am working with recurrent event data, using counting
process notation. Occasionally, (about 1 in every 100 simulations) I get the
following warning:
Error in coxph(Surv(start, end, censorind) ~ binary + uniform +
frailty(subject, :
X matrix deemed to be singular; variable 2
My data is structured as follows: I have a Bernoulli random variable
(parameter=0.5) (labeled "binary") and a second variable which was generated as
seq(0.02, 1, 0.02), which is labeled as "uniform". There are 50 individual
subjects. Recurrent events are then generated as rexp(1,
0.2*frailparm[j]*exp(mydata[j,1]*alpha[1]+mydata[j,2]*alpha[2])) where mydata
is the cbind of the data just mentioned, alpha are the parameters for the
recurrent events (here I am using c(1,1)) and frailparm is the frailty term for
subject {j}. I generate recurrent events until the sum of the times is greater
than the terminal time or censoring time, and keep the previous highest time as
my final recurrent time, with one additional time which is censored at the
minimum of the terminal event time and the censoring time. I then repeat for
each subject.
I then try to analyze the data like this:
coxph(Surv(start,end,censorind)~binary+uniform+frailty(subject,distribution="gauss",
method="reml"), method="breslow", singular.ok=FALSE, data=fulldata)
Where start is the previous recurrent time, end is the current recurrent time,
censorind is the censoring indicator for the current recurrent time, and
subject is the current observation.
There does not appear to be an issue with the binary variable taking a
particular value for every observed event time, nor does there appear to be
perfect correlation between the variable "uniform" and the survival time.
Any help would be much appreciated.
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