search for: fstatus

...to assess the effect of qualitative predictor (gg) with more than two categories (a,b,c) categorie c is the reference category. See above results, gg is considered like a ordered predictor ! Thank you for your help Jan > # simulated data to test > set.seed(10) > ftime <- rexp(200) > fstatus <- sample(0:2,200,replace=TRUE) > gg <- factor(sample(1:3,200,replace=TRUE),1:3,c('a','b','c')) > cov <- matrix(runif(600),nrow=200) > dimnames(cov)[[2]] <- c('x1','x2','x3') > cov2=cbind(cov,gg) > print(z <- crr(ftime,fs...

Hello everyone, (This is my second question posted today on the R list). I am carrying out a competing risks analysis using the cuminc function...this takes the form: cuminc(ftime,fstatus,group) In my study, fstatus has 3 different causes of failure (1,2,3) there are also censored cases (0). "group" has two levels (0 and 1). I therefore have 6 different cumulative incidence curves: cause 1, group=0; cause 1 group=1 cause 2, group=0; cause 2 group=1 cause 3, group=0; ca...

Hi, I would like to write a function which has the following syntax: myfn <- function(formula, ftime, fstatus, data) { # step 1: obtain terms in `formula' from dataframe `data' # step 2: obtain ftime from `data' # step 3: obtain fstatus from `data' # step 4: do model estimation # step 5: return results } The user would call this function as: myfn(formula=myform, ftime=myftime, fstatus=my...

Hi all I do not understand why I am getting the following error message. Can anybody help me with this? Thanks in advance. install.packages("cmprsk") library(cmprsk) result1 <-crr(ftime, fstatus, cov1, failcode=1, cencode=0 ) one.pout1 = predict(result1,cov1,X=cbind(1,one.z1,one.z2)) predict.crr(result1,cov1,X=cbind(1,one.z1,one.z2)) Error: could not find function "predict.crr" [[alternative HTML version deleted]]

Dear all, I am trying to use the crr function in the cmprsk package version 2.2 to analyse 198 observations.I have receive the error in solve.default. Can anyone give me some insights into where the problem is? Thanks here is my script : cov=cbind(x1,x2) z<-crr(ftime,fstatus,cov)) and data file: x1 x2 fstatus ftime 0 .02 1 263 0 .03 1 113 0 .03 1 523 0 .03 3 49 0 .03 1 278 0 .03 1 190 0 .04 1 472 0 .04 1 357 0 .04 1 219 0 .0429 3 49 0 .05 1 207 0 .05 1...

Dear R-help, I'm very sorry to ask 2 questions in a week. I am using the package 'crr' and it does exactly what I need it to when I use the dataset a. However, when I use dataset b I get the following error message: Error in drop(.Call("La_dgesv", a, as.matrix(b), tol, PACKAGE = "base")) : system is computationally singular: reciprocal condition number =

Hi All, I am running a scatter plot and trying to add a best fit line. I use an abline function, but get no line drawn over the points. I also get no error. I arm using V 2.10.0 on Windows 7. Here is my code, including the SAS transport file import: require (foreign) require (chron) require (Hmisc) require (lattice) clin <- sasxport.get("y:\\temp\\subset.xpt") attach(clin)

...plot.cuminc function in the CMPRSK library. The default x-axis places tick marks and labels at 0, 20, 40, 60, and 80 (my data has an upper limit of 96), whereas I want them at my own specified locations. Here is my example code: library(cmprsk) attach(MYDATA) MYCUMINC <- cuminc(ftime=TIME,fstatus=STATUS,group=GROUP,rho=0,cencode=0,na.action=na.omit) plot(MYCUMINC,xlim=c(0,96),ylim=c(0,0.5),xlab="",axes=F) axis(1,at=c(0,8,16,24,32,48,72,96)) As you can see, I have tried using the "axes=F" parameter that works for most plotting functions, but I get the following error m...

Dear everyone, My question involves the use of the survival object. We can have Surv(time,time2,event, type=, origin = 0) (1) As detailed on p.65 of: http://cran.r-project.org/web/packages/survival/survival.pdf My data (used in my study) is 'right censored' i.e. my variable corresponding to 'event' indicates whether a person is alive (0) or dead (1) at date last seen

Hi, To use cuminc() from cmprsk package, if a subject has 2 events (both the event of interest and the event of competing risk), should I create 2 observations for this subject in the dataset, one for each event with different fstatus (1 and 2), or just 1 observation with whatever event that happened first? My analysis objective is calculate cumulative incidence for the event of interest. Thanks! John

Hello everyone,   I am a very new user of R and I have a query about the cuminc function in the package cmprsk. In particular I would like to verify that I am interpreting the output correctly when we have a stratification variable.   Hypothetical example:   group : fair hair, dark hair fstatus: 1=Relapse, 2=TRM, 0=censored strata: sex (M or F)   Our data would be split into:   Fair, male, relapse Dark,male, relapse Fair, female, relapse Dark, female, relapse   Fair, male, TRM Dark,male, TRM Fair, female, TRM Dark, female, TRM   Fair, male, censored Dark,male, censored Fair, female, censo...

Hello everyone, I am a very new user of R and I have a query about the cuminc function in the package cmprsk. In particular I would like to verify that I am interpreting the output correctly when we have a stratification variable. Hypothetical example: group : fair hair, dark hair fstatus: 1=Relapse, 2=TRM, 0=censored strata: sex (M or F) Our data would be split into: Fair, male, relapse Dark,male, relapse Fair, female, relapse Dark, female, relapse Fair, male, TRM Dark,male, TRM Fair, female, TRM Dark, female, TRM Fair, male, censored Dark,male, censored Fair, female, censo...

...rection=c("forward"), crr.object = FALSE, trace = TRUE, steps = 100) crrstep(formula = years ~ 1 + var1+var2+var3+var4+var5, scope.min = ~1, etype = censorcmprsk, data = crisk, direction = c("forward"), crr.object = FALSE, trace = TRUE, steps = 100) *Error in crr(ftime, fstatus, cov1, variance = TRUE, ...) : unused argument(s) (variance = TRUE)* Thank you very much! [[alternative HTML version deleted]]

Hi, I am wondering if anyone can explain to me if cumulative incidence (CI) is just "1 minus kaplan-Meier survival"? Under what circumstance, you should use cumulative incidence vs KM survival? If the relationship is just CI = 1-survival, then what difference it makes to use one vs. the other? And in R how I can draw a cumulative incidence plot. I know I can make a Kaplan-Meier