similar to: Cumulative Incidence : Gray's test

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,

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.

Hello, I have a problem regarding calculation of Cumulative Incidence Function. The event of interest is failure of bone-marrow transplantation, which may occur due to relapse or death in remission. The data set that I have consists of- lifetime variable, two indicator variables-one for relapse and one for death in remission, and the other variables are donor type (having 3 categories), disease

Dear friends: I'm interested to make a stacked plot of cumulative incidence. that's, the cuminc model is fitted [fit=cuminc(time, relapse)] and cumulative incidence is in place. I'd like to stack the cuminc plots (relapse of luekemia and death free from leukemia, for example) , then the constituent ratio of leukemia relapse and treatment related mortality is very clear. Can

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:

Dear R-community, I would like to plot the numbers at risk for the different causes of failure at specific timepoints below a cumulative incidence function plot (plot.cuminc-function, cmprsk-package). For a Kaplan-Meier plot I know this is possible with the n.risk-argument in the survplot-function (rms-package), but to my knowledge no such readily-available functions are available for competing

Dear R-help list members, I am trying to create my own axes when plotting a cumulative incidence curve using the 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 <-

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

Hi, I want to write own R functions for cumulative incidence function and also for the pseudovalue of the cumulative incidence function. Can you help me? Tas. [[alternative HTML version deleted]]

Hi everyone, I am using the package cmprsk in R to estimate the cumulative incidence function and its variance. In the manual it is mentioned that the variance is calculated based on Dr. Aalen's paper (1978, Nonparametric estimation of partial transition probabilities in multiple decrement models). I would appreciate if someone could provide me with a source where the variance is expressed

Dear R users, I d like to assess the effect of "treatment" covariate on a disease relapse risk with the package cmprsk. However, the effect of this covariate on survival is time-dependent (assessed with cox.zph): no significant effect during the first year of follow-up, then after 1 year a favorable effect is observed on survival (step function might be the correct way to say that

Dear R users, I d like to assess the effect of "treatment" covariate on a disease relapse risk with the package cmprsk. However, the effect of this covariate on survival is time-dependent (assessed with cox.zph): no significant effect during the first year of follow-up, then after 1 year a favorable effect is observed on survival (step function might be the correct way to say that ?).

Hello again, I m trying to use timereg package as you suggested (R2.7.1 on XP Pro). here is my script based on the example from timereg for a fine & gray model in which relt = time to event, rels = status 0/1/2 2=competing, 1=event of interest, 0=censored random = covariate I want to test library(timereg) rel<-read.csv("relapse2.csv", header = TRUE, sep = ",",

Hi I am estimating the following coxph function with stratification and frailty?where each person had multiple events. m<-coxph(Surv(dtime1,status1)~gender+cage+uplf+strata(enum)+frailty(id),xmodel) ? > head(xmodel) id enum dtime status gender cage uplf 1 1008666 1 2259.1412037 1 MA 0.000 0 2 1008666 2 36.7495023 1 MA 2259.141 0 3 1008666

Hello, I have a question regarding competing risk regression using cmprsk package (function crr()). I am using R2.9.1. How can I do 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)

Hi all, I'm very confused! I've been using the same code for many weeks without any bother for various covariates. I'm now looking at another covaraite and whenever I run the code you can see below I get an error message: "Error in rep(0, nrow(data)) : invalid 'times' argument" This code works: # remove 'missing' cases from data # snearma <-

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,

Hi All, I'm trying to figure out the cumulative incidence curve in R in some limited time. I found in package "cmprsk", the command "plot.cuminc" can get this curve. But I noticed that there is no mark for the censored time there, comparing with the KM curve by "plot.survfit". Here are my codes (attached is the data): ----------------

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 18000 genes. So I have the expressions of 18000 genes in each of the 80 tumors (matrix

Dear list, I am not familiar with survival analysis and I would need your help to understand a result I have obtained. I have used the following command line to look at number of events and probability of survival at the first 5 years: > su = summary(survfit(Surv(a[, Date], a[, Event]) ~ strata(a[,Prediction]), data = a), times=c(0,1,2,3,4,5)) I have studied two kind of events (disease-free