search for: subdistributions

...fall into the lower or upper subdistribution. To test if A and B really have different types of distributions, multiple subjects (20, say) respond to many items of each type (100 each, say). Each subject generates two distributions, A vs. B. I run the EM algorithm to find the means for the two subdistributions in each. The algorithm finds two means for both, say Am1, Am2, Bm1, Bm2. Since the algorithm is told to find two distributions, even if there's really only one (as hypothesized for B), it seems wrong to test H0: Am1 = Am2 vs. H0: Bm1 = Bm2 - I'd expect both to be rejected. Rather, I wan...

> Dear All, > > I have built a survival cox-model, which includes a covariate * time interaction. (non-proportionality detected) > I am now wondering how could I most easily get survival predictions from my model. > > My model was specified: > coxph(formula = Surv(event_time_mod, event_indicator_mod) ~ Sex + > ageC + HHcat_alt + Main_Branch + Acute_seizure +

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

Dear all, Happy New year! I have used the 'crr' function to fit the 'proportional subdistribution hazards' regression model described in Fine and Gray (1999). dat1 is a three column dataset where: - ccr is the time to event variable - Crcens is an indicator variable equal to 0 if the event was achieved, 1 if the event wasn't acheived due to death or 2 if the event wasn't

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

Justin, You have an interesting problem, and a serious (reliable) consultation would take more time than I have to give at the moment. Which is to say that you should take these comments with a grain of salt. First, I don't think that you have censored data. You have 2 subdistribution functions F1(t) and F2(t), F1(t) + F2(t) = F(t) = the "time to endpoint" distribution.

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 = ",",

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 ?).

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

CRAN (and crantastic) updates this week New packages ------------ * disclapmix (0.1) Maintainer: Mikkel Meyer Andersen Author(s): Mikkel Meyer Andersen and Poul Svante Eriksen License: GPL-2 http://crantastic.org/packages/disclapmix disclapmix makes inference in a mixture of Discrete Laplace distributions using the EM algorithm. * EstSimPDMP (1.1) Maintainer: Unknown Author(s):