similar to: Cumulative Incidence Function and Pseudovalue

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 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

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 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

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.

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

I have a competing risk data where a patient may die from either AIDS or Cancer. I want to compare the cox model for each of the event of interest with a competing risk model. In the competing risk model the cumulative incidence function is used directly. I used the jackknife (pseudovalue) of the cumulative incidence function for each cause (AIDS or Cancer) in a generalized estimating equation. I

I have a longitudinal competing risk data of the form: ID COMPL SEX HEREDITY 1 0 1 2 1 0 1 2 1 3 1 2 2 0 0 1 2 1 0 1 2 2 0 1 2 2 0 1 3 0 0 1 3 0 0 1 3 0 0 1 3 0 0 1 3 2 0 1 4 0 1 2 4 0 1

Dear all, I have a stat question that may not be related to R, but I would like to have your advice. I have just read a medical paper in which the authors report the 1-p (where p is the cumulative survival probability from the Kaplan Meier curve) as incidence of disease. Specifically, the study followed ~12000 women on drug A and ~20000 women on drug B for 12 months. During that period

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,

All: Though I am fairly new to R, I am trying to work my way through J Oksanen's "Incidence Function Model in R" and can't get past some error with my glm arguments. I'm getting through > attach(amphimedon_compressa) > plot(x.crd,y.crd,asp=1,xlab="Easting",ylab="Northing",pch=21,col=p+1,bg=5*p) > d<-dist(cbind(x.crd,y.crd)) > alpha<-1

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

Hi I am working on educational assignment to produce an *Incidence matrix *from a *BIB design *using R language software. I found a web page *http://wiki.math.yorku.ca/index.php/R:_Incidence_matrix * about the problem. But it produces Data matrix instead of Incidence matrix. can you please help me out using R software. thanks and regards zaheer *following are the sample codes of my assignment

Dear R-users, I am checking the proportional hazard assumption of a cox model for a given covariate, let say Z1, after adjusting for other relavent covariates in the model. To this end, I fitted cox model stratified on the discrete values of Z1 and try to get beslow estimator for the baseline cumulative hazard function (H(t)) in each stratum. As far as i know, if the proportionality assumption

I would like to compare the incidence rates of three groups. They are supposed to have different risks so I would like to test whether there is a increasing trend in the incidence rates. Does R or any packages provide a trend test for incidence rates? I checked epiR and epitools. It seems they do not have this function. Thank you for the help. -- View this message in context:

Hi, I have just started using R. Now I have the following problem: I want to create an Empirical Cumulative Distribution Function and I only came so far: F10 <- ecdf(x) plot(F10, verticals= TRUE, do.p = TRUE, lwd=3) x=c(1.6,1.8,2.4,2.7,2.9,3.3,3.4,3.4,4,5.2) Now I'd like to use arguments such as xlabs and main but I don't know how to integrate them. I hope someone can help me, I am

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: So i've fit a hazard function to a set of data using kmfit<-survfit(Surv(int, event)~factor(cohort)) this factor variable, "cohort" has four levels so naturally the strata variable has 4 values. I can use this data to estimate the hazard rate haz<-n.event/n.risk and calculate the cumulative hazard function by H<--log(haz) Now, I would like to plot this

Dear useRs, There is a new version 0.1-2 of the package surv2sample available on CRAN. Users of the previous versions should update because a bug in the function cif2.ks has been fixed. General information about the package: surv2sample provides various two-sample tests for right-censored survival data. Three main areas and corresponding methods are: * comparison of two survival

Dear useRs, There is a new version 0.1-2 of the package surv2sample available on CRAN. Users of the previous versions should update because a bug in the function cif2.ks has been fixed. General information about the package: surv2sample provides various two-sample tests for right-censored survival data. Three main areas and corresponding methods are: * comparison of two survival