On Thu, Aug 19, 2004 at 09:36:22AM -0300, Hanke, Alex wrote: > 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.
Right, the 'splitting' was made incorrectly, but 'coxph' shouldn't segfault anyway. The error seems to be (caught) in 'coxph_wtest.c', line 29, which may be of interest to the R maintainer of 'survival', Thomas L. Göran > > Alex > -----Original Message----- > From: Rui Song [mailto:[EMAIL PROTECTED] > Sent: August 19, 2004 12:21 AM > To: [EMAIL PROTECTED] > 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 > > ______________________________________________ > [EMAIL PROTECTED] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html > > ______________________________________________ > [EMAIL PROTECTED] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html -- Göran Broström tel: +46 90 786 5223 Department of Statistics fax: +46 90 786 6614 Umeå University http://www.stat.umu.se/egna/gb/ SE-90187 Umeå, Sweden e-mail: [EMAIL PROTECTED] ______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
