Displaying 20 results from an estimated 11000 matches similar to: "How to set type of censored data in coxph regression"
2004 May 21
1
interval-censored data in coxph
Hi,
I am wondering how to specify interval-censored data
in coxph? The example in the help page
summary(coxph(Surv(start, stop, event) ~ x, data =
test2))
is for counting process data, is the counting process
data the same as interval-censored data?
Thanks
__________________________________
2004 May 16
2
Error in using coxph()
Hi,
I am getting errors of the following kind. I can't
seem to point the source of the error. I would greatly
appreciate any advice.
Many thanks and good day,
-Melinda
Error message :
----------------
"Ran out of iterations and did not converge in:
fitter(X, Y, strats, offset, init, control, weights =
weights,..."
Details :
---------
E is a vector of survival times (or censored
2010 Jun 23
1
Probabilities from survfit.coxph:
Hello:
In the example below (or for a censored data) using survfit.coxph, can
anyone point me to a link or a pdf as to how the probabilities appearing in
bold under "summary(pred$surv)" are calculated? Do these represent
acumulative probability distribution in time (not including censored time)?
Thanks very much,
parmee
*fit <- coxph(Surv(futime, fustat) ~ age, data = ovarian)*
2005 Sep 07
1
Survival analysis with COXPH
Dear all,
I would have some questions on the coxph function for survival analysis,
which I use with frailty terms.
My model is:
mdcox<-coxph(Surv(time,censor)~ gender + age + frailty(area, dist='gauss'),
data)
I have a very large proportion of censored observations.
- If I understand correctly, the function mdcox$frail will return the random
effect estimated for each group on the
2006 Oct 27
1
Censored Brier Score and Royston/Sauerbrei's D
System: R 2.3.1 on a Windows XP computer.
I am validating several cancer prognostic models that have been
published with a large independent dataset. Some of the models report a
probability of survival at a specified timepoint, usually at 5 and 10
years. Others report only the linear predictor of the Cox model.
I have used Harrell's c index for censored data (rcorr.cens) as a
measure of
2012 Dec 03
1
fitting a gamma frailty model (coxph)
Dear all,
I have a data set<http://yaap.it/paste/c11b9fdcfd68d02b#gIVtLrrme3MaiQd9hHy1zcTjRq7VsVQ8eAZ2fol1lUc=>with
6 clusters, each containing 48 (possibly censored, in which case
"event = 0") survival times. The "x" column contains a binary explanatory
variable. I try to describe that data with a gamma frailty model as follows:
library(survival)
mod <-
2010 Nov 12
3
predict.coxph
Since I read the list in digest form (and was out ill yesterday) I'm
late to the discussion.
There are 3 steps for predicting survival, using a Cox model:
1. Fit the data
fit <- coxph(Surv(time, status) ~ age + ph.ecog, data=lung)
The biggest question to answer here is what covariates you wish to base
the prediction on. There is the usual tradeoff between too few (leave
out something
2004 Jun 07
1
Censboot Warning and Error Messages
Good day R help list!!!
I've been trying to do Bootstrap in R on Censored data. I encountered
WARNING/ERROR messages which I could not find explanation.
I've been searching on the literature for two days now and still can't find
answers. I hope there's anyone out there who can help me
with these two questions:
1. If the "Loglik converged before variable..." message
2012 May 02
1
coxph reference hazard rate
Hi,
In the following results I interpret exp(coef) as the factor that multiplies
the base hazard rate if the corresponding variable is TRUE. For example,
when the bucket is ks008 and fidelity <= 3, then the rate, compared to the
base rate h_0(t), is h(t) = 0.200 h_0(t). My question is then, to what case
does the base hazard rate correspond to? I would expect the reference to be
the first
2009 Jun 24
1
Coxph frailty model counting process error X matrix deemed singular
Hello,
I am currently trying to simulate data and analyze it using the frailty option in the coxph function. I am working with recurrent event data, using counting process notation. Occasionally, (about 1 in every 100 simulations) I get the following warning:
Error in coxph(Surv(start, end, censorind) ~ binary + uniform + frailty(subject, :
X matrix deemed to be singular; variable 2
My
2004 Apr 21
1
difference between coxph and cph
Hi. I am using Windows version of R 1.8.1. Being somewhat new to survival
analysis, I am trying to compare cph (Design) with coxph (survival) for use
with a survival data set.
I was wondering why cph and coxph provide me with different confidence
intervals
for the hazard ratios for one of the variables. I was wondering if I am
doing something wrong? Or if the two functions are calculating hazard
2010 Sep 23
2
extending survival curves past the last event using plot.survfit
Hello,
I'm using plot.survfit to plot cumulative incidence of an event.
Essentially, my code boils down to:
cox <-coxph(Surv(EVINF,STATUS) ~ strata(TREAT) + covariates, data=dat)
surv <- survfit(cox)
plot(surv,mark.time=F,fun="event")
Follow-up time extends to 54 weeks, but the last event occurs at week
30, and no more people are censored in between. Is there a
2007 Nov 29
1
Survreg(), Surv() and interval-censored data
Can anybody give me a neat example of interval censored data analysis codes in R?
Given that suvreg(Surv(c(1,1,NA,3),c(2,NA,2,3),type="interval2")~1)
works why does
survreg(Surv(data[,1],data[,2],type="interval2")~1)
not work where
data is :
T.1 T.2 Status
1 0.0000000 0.62873036 1
2 0.0000000 2.07039068 1
3 0.0000000
2011 Dec 19
1
Calculating the probability of an event at time "t" from a Cox model fit
Dear R-users,
I would like to determine the probability of event at specific time using
cox model fit. On the development sample data I am able to get the
probability of a event at time point(t).
I need probability score of a event at specific time, using scoring scoring
dataset which will have only covariates and not the response variables.
Here is the sample code:
n = 1000
beta1 = 2; beta2 =
2006 Feb 13
2
Survreg(), Surv() and interval-censored data
Can survreg() handle interval-censored data like the documentation
says? I ask because the command:
survreg(Surv(start, stop, event) ~ 1, data = heart)
fails with the error message
Invalid survival type
yet the documentation for Surv() states:
"Presently, the only methods allowing interval censored data are
the parametric models computed by 'survreg'"
2013 Apr 15
2
regression with paired left-censored data
HI
I am trying to analyse data which is left-censored (i.e. has values below the detection limit). I have been using the NADA package of R to derive summary statistics and do some regression. I am now trying to carry out regression on paired data where both my X and Y have left-censored data within them.
I have tried various commands in R:
rega = cenreg(Cen(conc, cens_ind) ~ Gp_ident))?
with
2012 May 11
2
survival analysis simulation question
Hi,
I am trying to simulate a regression on survival data under a few
conditions:
1. Under different error distributions
2. Have the error term be dependent on the covariates
But I'm not sure how to specify either conditions. I am using the Design
package to perform the survival analysis using the survreg, bj, coxph
functions. Any help is greatly appreciated.
This is what I have so far:
2001 Mar 11
2
Kaplan-Meier for left-truncated, right-censored data
Is it possible to calculate Kaplan-Meier for left-truncated,
right-censored data using survival5?
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2005 Jan 16
2
Empirical cumulative distribution with censored data
Dear list,
I would like to plot the empirical cumulative distribution of the time
needed by a treatment to attain a certain goal. A number of
experiments is run with a strict time limit. In some experiments the
goal is attained before the time limit, in other experiments time
expires before the goal is attained. The situation is very similar to
survivial analysis with censored data. I tryed
2012 Feb 23
1
Schoenfeld residuals for a null model coxph
Hi,
I have a coxph model like
coxph(Surv(start, stop, censor) ~ x + y, mydata)
I would like to calculate the Schoenfeld residuals for the null, i.e the same model where the beta hat vector (in practical terms, the coeff vector spat out by summary()) is constrained to be all 0s --all lese stays the same.
I could calculate it by hand, but I was wondering if there is a way of doing it with