It's also "well described" in the help materials for the obvious
recommended package that ships with every copy of R. My copy sits at
http://127.0.0.1:29434/library/survival/doc/timedep.pdf. Therneau's S
package was first ported to R by Thomas Lumley and later Therneau took
over maintenance.
Stop re-posting this question. It only irritates people... it does not improve
your chances of getting help.
What does improve your chances is reading the Posting Guide and following the
advice given there. Your question amounts to asking someone to figure out what
theory you should apply to a
It's also "well described" in the help materials for the obvious
recommended package that ships with every copy of R. My copy sits at
http://127.0.0.1:29434/library/survival/doc/timedep.pdf. Therneau's S
package was first ported to R by Thomas Lumley and later Therneau took
over maintenance.
I need a R-code for a situation that is well described in the sas help. I
would be very grateful for the help!
"Time-dependent variables can be used to model the effects of subjects
transferring from one treatment group to another. One example of the need
for such strategies is the Stanford heart t
I need a R-code for a situation that is well described in the sas help. I
would be very grateful for the help!
"Time-dependent variables can be used to model the effects of subjects
transferring from one treatment group to another. One example of the need
for such strategies is the Stanford heart t
I need a R-code for a situation that is well described in the sas help. I
would be very grateful for the help!
"Time-dependent variables can be used to model the effects of subjects
transferring from one treatment group to another. One example of the need
for such strategies is the Stanford heart t
Dear R-help Community,
I'm currently struggling with some issues extending the proportional Cox model
with time-dependent coefficients and could really need some help.
Since I'm not experienced in adding code in an email in a nice way I add the
link to my question and code:
https://stats.stac
On 12/23/2014 05:00 AM, r-help-requ...@r-project.org wrote:
Dear all,
I'm using the package "survival" for adjusting the Cox model with multiple
events (Prentice, Williams and Peterson Model). I have several covariates,
some of them are time-dependent.
I'm using the function"cox.zph" to check
Dear all,
I'm using the package "survival" for adjusting the Cox model with multiple
events (Prentice, Williams and Peterson Model). I have several covariates,
some of them are time-dependent.
I'm using the function"cox.zph" to check the proportional hazards. Due to
the nature of the time-depende
Comment inline
On 19/12/2014 11:17, aoife doherty wrote:
Many thanks, I appreciate the response.
When I convert the missing values to NA and run the cox model as described
in previous post, the cox model seems to remove all of the rows with a
missing value (as the number of rows "n" in the cox
Yes, your basic reasoning is correct. In general, the observed variables
carry information about the variables with missing values, so (in some
way) the missing values can be replaced with estimates ("imputations")
and the standard regression method will then work as though the
replacements were th
Many thanks, I appreciate the response.
When I convert the missing values to NA and run the cox model as described
in previous post, the cox model seems to remove all of the rows with a
missing value (as the number of rows "n" in the cox output after I
completely remove any row with missing data
Hi Aoife,
I think that if you simply replace each "*" in the data file
with "NA", then it should work ("NA" is usually interpreted
as "missing" for those functions for which missingness is
relevant). How you subsequently deal with records which have
missing values is another question (or many quest
First recode the * in NA: death.dat$v3[death.dat$v1==*] <- NA
Include this in your model: na.rm=TRUE
Or you could create a new dataset: newdata <- na.omit(death.dat)
Shouro
On Fri, Dec 19, 2014 at 11:12 AM, aoife doherty
wrote:
>
> Hi all,
>
> I have a data set like this:
>
> Test.cox file
Hi all,
I have a data set like this:
Test.cox file:
V1V2 V3 Survival Event
ann 13 WTHomo 41
ben 20 *51
tom 40 Variant 61
where "*" in
Question about package Coxme:
I develop a cox model that includes a variable "treatment" with 3 levels (A, B,
C):
> model_alea_int <- coxme(Surv(delai, status) ~ (1|trt)+ strata(center) , data)
I am surprised that the output given in R is 3 coefficients for random effects
whereas only 2 dummy
On 02/16/2013 05:00 AM, r-help-requ...@r-project.org wrote:
Then I perform cox regression as follows
m2_1<-coxph(Surv(X_t0,X_t, vlsupp) ~ nvp + as.factor(cd4pccat) +
as.factor(vlcat) + as.factor(agecat) + as.factor(whostage) +
as.factor(hfacat) + as.factor(wfacat) + as.factor(wfhcat) +
as.fac
I have a question about Cox's partial likelihood approximations in "coxph"
function of "survival package (and in SAS as well) in the presence of tied
events generated by grouping continuous event times into intervals.
I am processing estimations for recurrent events with time-dependent
covariates i
On Fri, Jul 22, 2011 at 2:04 PM, Terry Therneau wrote:
> For time scale that are truly discrete Cox proposed the "exact partial
> likelihood".
Or "the method of partial likelihood" applied to the discrete logistic model,
> I call that the "exact" method and SAS calls it the
> "discrete" method.
> From: thern...@mayo.edu
> To: abouesl...@gmail.com
> Date: Fri, 22 Jul 2011 07:04:15 -0500
> CC: r-help@r-project.org
> Subject: Re: [R] Cox model approximaions (was "comparing SAS and R
> survival)
>
>
For time scale that are truly discrete Cox proposed the "exact partial
likelihood". I call that the "exact" method and SAS calls it the
"discrete" method. What we compute is precisely the same, however they
use a clever algorithm which is faster. To make things even more
confusing, Prentice int
Thanks a lot for your answer.
Martin Patenaude-Monette
MSc. Candidate
Département de biologie
Université du Québec à Montréal
2011/3/15 Terry Therneau
> --- included text --
> I have done model selection between candidate Cox models, using AICc
> calculated with penalized log likelihoods. The
--- included text --
I have done model selection between candidate Cox models, using AICc
calculated with penalized log likelihoods. Then model averaging was done
to
obtain model averaged parameter estimates. Is there a way to plot
survival
curve from the averaged model, by estimating baseline haza
Dear community,
I have done model selection between candidate Cox models, using AICc
calculated with penalized log likelihoods. Then model averaging was done to
obtain model averaged parameter estimates. Is there a way to plot survival
curve from the averaged model, by estimating baseline hazard a
Dear R users,
Here is the coxme output I obtain on my survival dataset having 3 strains
and 2 infection status (i: infected, ni: non infected)
coxme(Surv(lay) ~ infection*strain, data=datalay, random= ~1 |block)
Cox mixed-effects model fit by maximum likelihood
Data: datalay
n= 1194
Iteration
> I am trying to build a cox model and then perform ROC analysis in order to
> retrieve some genes that are correlated with breast cancer. When I calculate
...
Extension of ROC values to the censored data case is handled by the rcorr.cens
function found in the Hmisc library. See the reference
Dear list,
I am trying to build a cox model and then perform ROC analysis in order to
retrieve some genes that are correlated with breast cancer. When I calculate
the hazard score taking into account different numbers of genes and their
coefficients ( I am trying to find the pest predictor number
On 2/13/2008 9:08 AM, Gustaf Rydevik wrote:
> On Feb 13, 2008 3:06 PM, Gustaf Rydevik <[EMAIL PROTECTED]> wrote:
>> On Feb 13, 2008 2:37 PM, Matthias Gondan <[EMAIL PROTECTED]> wrote:
>> > Hi Eleni,
>> >
>> > The problem of this approach is easily explained: Under the Null
>> > hypothesis, the P va
On Feb 13, 2008 3:06 PM, Gustaf Rydevik <[EMAIL PROTECTED]> wrote:
> On Feb 13, 2008 2:37 PM, Matthias Gondan <[EMAIL PROTECTED]> wrote:
> > Hi Eleni,
> >
> > The problem of this approach is easily explained: Under the Null
> > hypothesis, the P values
> > of a significance test are random variable
On Feb 13, 2008 2:37 PM, Matthias Gondan <[EMAIL PROTECTED]> wrote:
> Hi Eleni,
>
> The problem of this approach is easily explained: Under the Null
> hypothesis, the P values
> of a significance test are random variables, uniformly distributed in
> the interval [0, 1]. It
> is easily seen that the
Hi Eleni,
The problem of this approach is easily explained: Under the Null
hypothesis, the P values
of a significance test are random variables, uniformly distributed in
the interval [0, 1]. It
is easily seen that the lowest of these P values is not any 'better'
than the highest of the
P values
Hmm...I see. I think I will give a try to the univariate analysis
nonetheless...I intend to catch the p-values for each gene and select the
most significant from these...I have seen it in several papers.
Best Regards,
Eleni
On Feb 13, 2008 2:59 PM, Terry Therneau <[EMAIL PROTECTED]> wrote:
> Wh
What you appear to want are all of the univariate models. You can get this
with a loop (and patience - it won't be fast).
ngene <- ncol(genes)
coefmat <- matrix(0., nrow=ngene, ncol=2)
for (i in 1:ngene) {
tempfit <- coxph(Surv(time, relapse) ~ genes[,i])
coefmat[i,] <- c(tempf
Dear Eleni,
from a previous post regarding maximum number of variables in a
multiple linear regression analysis, posted last tuesday, and I think
it can be relevant also to Cox PH models:
"I can think of
no circumstance where multiple regression on "hundreds of thousands of
variables" is anythi
Hi David,
The problem is that I need all these regressors. I need a coefficient for
every one of them and then rank them according to that coefficient.
Thanks,
Eleni
On Feb 12, 2008 4:54 PM, <[EMAIL PROTECTED]> wrote:
> Hi Eleni,
>
> I am not an expert in R or statistics but in my opinion you h
Hi Eleni,
I am not an expert in R or statistics but in my opinion you have too
many regressors compared to the number of observations and that might
be the reason why you get the error. Others might say better but as
far as I know, having only 80 observations, it is a good idea to first
filter
Hello R-community,
It's been a week now that I am struggling with the implementation of a cox
model in R. I have 80 cancer patients, so 80 time measurements and 80
relapse or no measurements (respective to censor, 1 if relapsed over the
examined period, 0 if not). My microarray data contain around
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