I haven't seen anyone solve this. I think it would be reasonable to do a
time point by time point averaging (over multiple imputations) of the
underlying survival curve, although there is some question about whether to
"freeze" the centering constant (sum of beta times covariate mean). What
will
Can anyone help with this ?
On 14/02/2013 14:07, Robert Long wrote:
I am working with some survival data with missing values.
I am using the mice package to do multiple imputation.
I have found code in this thread which handles pooling of the MI results:
https://stat.ethz.ch/pipermail/r-help/2
I am working with some survival data with missing values.
I am using the mice package to do multiple imputation.
I have found code in this thread which handles pooling of the MI results:
https://stat.ethz.ch/pipermail/r-help/2007-May/132180.html
Now I would like to plot a survival curve using th
The time-transform (tt() arguments) feature is the most recent addition
to coxph. Most of the follow-up functions, in particular survfit(fit)
have not yet been updated to deal with such models. Your message points
out that I need to at least update them to add a "not yet available"
error message
Dear all,
Let's assume I have a clinical trial with two treatments and a time to
event outcome. I am trying to fit a Cox model with a time dependent
treatment effect and then plot the predicted survival curve for one
treatment (or both).
library(survival)
test <-
list(time=runif(100,0,10),e
Hi:
Here's one way to put all the plots in one graph using ggplot2 and a
couple of tricks using the plyr package. You could take the data frame
I generate below and use it as input to lattice graphics if you
prefer. For groupwise plots, either as an ensemble or as separate
panels, these packages a
Yes. Trellis plots are in the "lattice" package. My bad.
-- Bert
On Tue, Jul 5, 2011 at 3:22 PM, Steve Lianoglou
wrote:
> Quick note:
>
> On Tue, Jul 5, 2011 at 6:16 PM, Bert Gunter wrote:
>> Yes, it can be done using basic plot commands.
>>
>> But if you really want to get fancy and plot "grou
On Jul 5, 2011, at 6:24 PM, David Winsemius wrote:
On Jul 5, 2011, at 6:08 PM, Trey Batey wrote:
Hello.
This is a follow-up to a question I posted last week. With some
previous suggestions from the R-help community, I have been able to
plot survival (, hazard, and density) curves using pub
On Jul 5, 2011, at 6:08 PM, Trey Batey wrote:
Hello.
This is a follow-up to a question I posted last week. With some
previous suggestions from the R-help community, I have been able to
plot survival (, hazard, and density) curves using published data for
Siler hazard parameters from a number
Quick note:
On Tue, Jul 5, 2011 at 6:16 PM, Bert Gunter wrote:
> Yes, it can be done using basic plot commands.
>
> But if you really want to get fancy and plot "grouped" graphs, I
> strongly recommend you look into R's packages -- ggplot or trellis.
Attempting to clear out any confusion before
Yes, it can be done using basic plot commands.
But if you really want to get fancy and plot "grouped" graphs, I
strongly recommend you look into R's packages -- ggplot or trellis.
Both have excellent documentation and companion books and were built
for this sort of thing. The (considerable) learn
Hello.
This is a follow-up to a question I posted last week. With some
previous suggestions from the R-help community, I have been able to
plot survival (, hazard, and density) curves using published data for
Siler hazard parameters from a number of ethnographic populations.
Can the function belo
On Jun 28, 2011, at 6:26 PM, David Winsemius wrote:
On Jun 28, 2011, at 5:46 PM, Trey Batey wrote:
Hello.
I am trying to write an R function to plot the survival function (and
associated hazard and density) for a Siler competing hazards model.
This model is similar to the Gompertz-Makeham,
On Jun 28, 2011, at 5:46 PM, Trey Batey wrote:
Hello.
I am trying to write an R function to plot the survival function (and
associated hazard and density) for a Siler competing hazards model.
This model is similar to the Gompertz-Makeham, with the addition of a
juvenile component that includes
Hello.
I am trying to write an R function to plot the survival function (and
associated hazard and density) for a Siler competing hazards model.
This model is similar to the Gompertz-Makeham, with the addition of a
juvenile component that includes two parameters---one that describes
the initial in
You have (unimportant lines omitted)
> c2= survdiff(Surv(act.surv.time,censoring)~treatgrp ,data=b)
> plot(c2)
The problem is that you are using the wrong function. It is survfit that
creates plottable survival curves, survdiff only does the log-rank test.
Terry Therneau
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