In case this time-dependent covariate is an internal time-dependent
covariate (aka endogenous time-dependent covariate), you can use the
jointModel() function from package JM, with the option "weibull-AFT-GH"
for the 'method' argument.
For more information you may have a look at:
http://rwiki.sciviews.org/doku.php?id=packages:cran:jm
I hope it helps.
Best,
Dimitris
Philipp Rappold wrote:
Dear all,
I have some trouble using the "id"-argument with aftreg (accelerated
failure time regression analysis from the eha library).
As far as I understand it, the id argument is used to group individuals
together if there are time-varying covariates and the data is arranged
in counting process style.
Unfortunately, i cannot figure out how to use the "id"-argument. The
most straight-forward way would be to simply state the grouping
variable, but it throws an error. I've included an example below: the
dataframe for regression is called "test", with the grouping variable
"person".
> test
start end censor person var1
1 0 1 0 1 0.5
2 1 2 0 1 0.4
3 2 3 0 1 0.6
4 3 4 1 1 -0.3
5 0 1 0 2 0.6
6 1 2 0 2 0.7
7 2 3 0 2 0.6
> fit <- aftreg(Surv(start, end, censor)~var1, data=test, id=person)
Error in order(id, Y[, 1]) : argument 1 is not a vector
> fit <- aftreg(Surv(start, end, censor)~var1, data=test,
id=test["person"])
Error in `[.data.frame`(id, ord) : undefined columns selected
What would be the correct way to fit this example model?
Thanks + all the best
Philipp
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--
Dimitris Rizopoulos
Assistant Professor
Department of Biostatistics
Erasmus University Medical Center
Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands
Tel: +31/(0)10/7043478
Fax: +31/(0)10/7043014
______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
