similar to: Plotting survival curves from a Cox model with time dependent covariates

The combination of survfit, coxph, and factors is getting confused. It is not smart enough to match a new data frame that contains a numeric for sitenew to a fit that contained that variable as a factor. (Perhaps it should be smart enough to at least die gracefully -- but it's not). The simple solution is to not use factors. site1 <- 1*(coxsnps$sitenew==1) site2 <-

Hello, I'm trying to estimate a Cox proportional hazard model with time-varying covariates using coxph. The parameter estimates are fine but there is something wrong with the survival curves I get with survfit (results are not plausible). Let me explain why I think something's wrong. To make sure I'm setting up my data correctly to estimate a model with time-varying covariates, I

I am trying to use the survfit() function with the newdata argument to produce predicted survivor curves for a particular covariate profile. The main purpose of the plot will be to visualise the effect of snp1, coded 0 and 1. In my Cox model I have stratified by one variable, edu, and so I know I will automatically get a separate curve for each strata. My problem is how to deal with the

I am trying to import a permanent SAS data set using read.ssd in the foreign library. I get the following error: > list.files("C:/temp") [1] "newdat1.sas7bdat" "snpm1.sas7bdat" "test1.sas" > library(foreign) > newdat1 <- read.ssd("C:/temp","newdat1") SAS failed. SAS program at

Dear all, Is there a way to calculate the non-cumulative hazard (instantaneous hazard), which is the product of baseline hazard and exp{beta*covariate} ? I knew in survfit, we can get the estimator of cumulative baseline hazard, but how can we get the non-cumulative one? Thank you very much! Koshihaku -- View this message in context:

Hi R Users, I have produced a simulated scatter plot of y versus x tightly clustered around the 45 degree line through the origin with the following code: x <- seq(1,100) y <- x+rnorm(100,0,10) plot(x,y,col="blue") abline(0,1) Is there some way to generate a 3-dimensional analogue of this? Can I get a similar simulated scatter plot of points in 3 dimensions where the points

Dear r-users, Does anyone know how to draw time-dependent survival curves? Example: Event outcome: CHD Time-dependent covariate: NSAID use, which changes over time for each subject I'm interested in survival curves stratified by NSAID use. I'd like to implement Simon & Makuch (1984) method. Is there a R package/function to draw this graph?

Hello, coxph does not use any information that are in the dataset between event times (or "death times") , since computation only occurs at event times. For instance, removing observations when there is no event at that time in the whole dataset does not change the results: > set.seed(1) > data <- as.data.frame(cbind(start=c(1:5,1:5,1:4),stop=c(2:6,2:6,2:5),status=c(rep(

Dear colleagues, following John Fox' advice in this article (http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-cox-regression.pdf), I'm trying to create a new data frame to examine the differential survival curves from a combination of covariates. These are derived from a Cox Proportional Hazards model I fit to data about the diffusion of a particular policy across American

I have a dataset with event=death time (from medical examination until death/censoring) dose (given at examination time) Two groups are considered, a non-exposed group (dose=0), an exposed group (dose between 5 and 60). For some reason there is a theory of the dose increasing its effect over time (however it was only given (and measured) once = at the time of examination). I tested a model:

Suppose I have a dataset contain three variants, looks like > head(dta) Sex tumorsize Histology time status 0 1.5 2 12.1000 0 1 1.8 1 38.4000 0 ..................... Sex: 1 for male; 0 for female., two levels Histology: 1 for SqCC; 2 for High risk AC; 3 for low risk AC,

Dear all, I would like to know if it is possible to fit in R a Cox ph model with time-dependent covariates and to account for hierarchical effects at the same time. Additionally, I'd like also to know if it would be possible to perform any feature selection on this model fit. I have a data set that is composed by multiple marker measurements (and hundreds of covariates) at different time

Let me expand a bit on Thomas's answer. Looking more closely at your data set you have the following: death time group 0 group 1 1.5 0/4 13/13 3 0/4 5/5 8 4/4 0 At time 1.5 group 1 had 13 deaths out of 13 at risk, group 0 had none. Time 8 doesn't have any impact on the fit, since only one group

Hello R users Would somebody know how to estimate survival from a frailty cox model, using the function survfit and the argument newdata ? (or from any other way that could provide individual expected survival with standard error); Is the problem related to how the random term is included in newdata ? kfitm1 <- coxph(Surv(time,status) ~ age + sex + disease + frailty(id,

Hi all, I am looking at the tutorial/appendix from John Fox on ?Cox Proportional-Hazards Regression for Survival Data? available here: http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-cox-regression.pdf I am particularly interested in modelling survival with time-dependent covariates (Section 4). The data look like this: > Rossi.2[1:50,] start stop arrest.time week arrest fin

Hi, May I know how to obtain the confidence interval of the survival curve of weighted Cox regression model? I tried coxph, cph, and coxphw, but they did not work. Any help would be much appreciated. Thanks, Jeanne -- View this message in context: http://r.789695.n4.nabble.com/Confidence-interval-of-Survival-Curve-of-Weighted-Cox-Regression-tp3308085p3308085.html Sent from the R help mailing

Hi there, I have a predictor varible "class" which is a categorical variable and a ' coxph' is used to find the coeffients. How can I plot the predicted survival proportion based on this model? Thanks Lisa Wang Princess Margaret Hospital Toronto tel 416 946 4501

Hi, I'm trying to compare models, one of which has all parameters fixed using offsets. The log-likelihoods seem reasonble in all cases except the model in which there are no free parameters (model3 in the toy example below). Any help would be appreciated. Cheers, Jarrod x<-rnorm(100) y<-rnorm(100, 1+x) model1<-lm(y~x) logLik(model1) sum(dnorm(y, predict(model1),

Hi: I have a question about the use of the survfit function after the estimation of a cox proportional hazard model with a frailty term. My goal is to estimate expected survival probabilities while controlling for the group-specific frailty term. First, I estimate a model of the following form: model1 <- coxph(Surv(t0, t, d) ~ x1 + x2 + frailty(id), na.action=na.exclude,

Hello, We are using frailty models to estimate risk of one year death. Is there a way to generate survival curves adjusted for covariates and also include frailty term? Any help will be much appreciated! Thanks! LV [[alternative HTML version deleted]]