[R] A question about external time-dependent covariates in co x model

[Hanke, Alex]

Dear Rui,
>From my understanding of time-dependent covariates (not an expert but have
been working on a similar problem), it would appear that the coding of the
status column is not correct. Unless you have observed an event at each
interval you should only have status=1 for the last interval. In your
example I see 3 in total. Also, I think that if "end" is proportional to
your "covariate" you are incorporating a redundant time effect into the
model. The time effect is in the baseline hazard.

Alex
-----Original Message-----
From: Rui Song [mailto:rsong at stat.wisc.edu] 
Sent: August 19, 2004 12:21 AM
To: r-help at stat.math.ethz.ch
Subject: [R] A question about external time-dependent covariates in cox
model


Dear Sir or Madam:
I am a graduate student in UW-Madison statistics department. I have a
question about fitting a cox model with external time-dependent
covariates.

Say the original data is in the following format:
Obs Eventtime  Status  Cov(time=5)  Cov(time=8)  Cov(time=10)	Cov(time=12)
1	5	1		2
2	8	0(censored)	2	4
3	10	1		2	4		6
4	12	1		2	4		6		8
....

Notice that the time-dependent covariates are identical at the same
time points for all obs since they are external to the failure process.
process.

Then I organized the data as the following:
obs	start	end	eventtime	status	cov
1	0	5	5		1	2
2	0	5	8		0	2
2	5	8	8		0	4
3	0	5	10		1	2
3	5	8	10		1	4
3	8	10	10		1	6
4	0	5	12		1	2
4	5	8	12		1	4
4	8	10	12		1	6
4	10	12	12		1	8

And fit the model using:

fit<-coxph(Surv(start, end, status)~cov);

When I fit the model to my data set (Which has 89 observations and 81
distinct time points, sort of large.), I always got a message that
"Process R segmentation fault (core dumped)". Would you let me know if it
is due to the matrix sigularity in the computation of the partial
likelihood or something else? And how should I fit a cox model with
external time-dependent covariates?

Thanks a lot for your time and help!

Sincerely,
Rui Song

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