search for: survsplit

Dear All, since years I am struggling with Surv objects in data.frames. The following seems to have to do with it. See below the modified example from the help page of survSplit. The original works, as expected. If, however, a Surv object is added to the data.frame, each record gets doubled. Is there some solution other than avoiding Surv objects in data.frames? Thanks, Heinz require(survival) ## from the help page aml3<-survSplit(aml,cut=c(5,10,50),end="time...

...s a first shot at an epidemiology package for R available at http://biostat.ku.dk/~bxc/SPE/library/, but built under 1.9. I recently tried to build it under 2.0, but it crashed for me and I was advised to wait till 2.1 before I had another go at it. A little further exploration of what goes on in survSplit gives: > data(aml) > aml$id <- letters[1:dim(aml)[1]] > aml3<-survSplit(aml,cut=c(5,10,50),end="time",start="start", + event="status",episode="i") > # Sort the rows sensibly > aml3 <- aml3[order(aml3$id,aml3$start),...

Dear R-users, I use the survsplit function in the survival package to change my data into counting-process format and the transformed format is as follow: (a) start stop event DP age .... 0 5 0 1 20 5 10 0 1 20 10 25 1 1 20 looking at the above three...

Danardono <daodao99 at student.umu.se> wrote: > > While waiting for 2.1, for those who need function[s] for this > survival-splitting business, as I do, this 'survcut' function below > might be helpful. > It is not an elegant nor efficient function but it works, at least for > some examples below. > Ditto the following, for the case where there are multiple

...dicts) id clinic status survtime prison meth clinic01 1 1 1 1 428 0 50 1 2 2 1 1 275 1 55 1 3 3 1 1 262 0 55 1 into the "counting data format" necessary to perform extended Cox regression. I got survSplit() to work, but it appears to have a problem with end times on the boundaries of the intervals. (My intervals are [0,183], [184,365], [366,548], [549,730], [730,Inf]). I looked in the searchable archives and found that others had also discovered problems with survSplit() and suggested Lexis() in...

Dear All, I got a warning message "X matrix deemed to be singular" in Cox model with a time dependent coefficient. In my analysis, the variable "SEX" is a categorical variable which violate the PH assumption in Cox. I first used the survSplit() function to break the data set into different time intervals, and then fit the model. The procedures can be described as follows: > data <- survSplit(Surv(time, status)~., data=data, cut=c(5, 10), episode= "tgroup", id="id") #data split > data$SEX <- as.factor(...

...coef exp(coef) se(coef) z p age_diag 0.0374 1.04 0.0124 3.03 0.0025 deeppresent 0.8639 2.37 0.3012 2.87 0.0041 ldhelevated 0.7222 2.06 0.2836 2.55 0.0110 Likelihood ratio test=21 on 3 df, p=0.000105 n= 91 To fit time dependent model, I used the survSplit() function to format dtemp into count data format: > cdf<-survSplit( cut=1:213, #cut at each month (last time is 213.9) end="time", start="start", event="status", data=dtemp) > cdf[order(cdf$id),][...

Hi, I am new to time-dependent Cox model to estimate time dependent hazard ratios. Let me use aml dataset from survival package: > aml3<-survSplit(aml2,cut=c(5,10,20),end="time",start="start", event="status",episode="i") If I want to esimate hazard ratio for each of the time intervals 0-5, 5-10, 10-20 and >=20, would the following calculate such HRs? > coxph(Surv(start,time,status)~x*as.fa...

Dear R list, I have an event history data set that is structured like this: Legislative act Discussion Agreement Time Event Act1 2006-05-30 2006-06-19 20 1 Act2 2004-03-01 2004-06-14 105 1 . . . I have information on the meetings in the legislature between adoption periods in a separate variable (the

Hello, I am trying to model an intensity function with time-varying covariates. Before, I have successfully defined a log likelihood function for a Power-Law Process (lambda(t)=alpha*beta*t^(beta-1)) with two paramters and no covariates for a repairable systems with failure times (t). This function was maximized with R optim. No problem! But now I want to include a covariate indicating a

...a (tiny) example of what I'm trying to do: # Original survival data set: > dat time status x1 1 169 1 2 2 149 1 11 3 207 1 22 4 192 1 27 5 200 1 10 # Split original data at cutpoint 190. New data will be in "counting process" format: > dat.x = survSplit(dat, cut=190, end="time", event="status", start="start") # New data set: > dat.x time status x1 start 1 169 1 2 0 2 149 1 11 0 3 190 0 22 0 4 190 0 27 0 5 190 0 10 0 8 207 1 22 190 9 192 1 2...

...ersion of the survival package has been released. The biggest change is stronger support for multi-state models, which is an outgrowth of their increasing use in my own practice. Interested users are directed to the "time dependent covariates" vignette for discussion of the tmerge and survSplit functions, which are useful tools to build the requisite data sets, and to the "multi-state" vignette for model fits and plots. Terry Therneau

...y models I use the time depended AUC calculated by the "score" function from package riskRegression. It internally uses a prediction function but when applying it to the extended model and the new test set it says "strata(time group) not found". When splitting my test set with survSplit the same way I did for the training set, it works and I get a nice AUC plot. But I am afraid if this actually makes sense and I don't wrongly include future information I usually don't have. I'm thankful for any help or hint. Kind regards Theresa

Hi, How can I specify a Cox proportional hazards model with a covariate which i believe its strength on survival changes/diminishes with time? The value of the covariate was only recorded once at the beginning of the study for each individual (e.g. at the diagnosis of the disease), so I do not have the time course data of the covariate for any given individual. For example, I want to state at the