search for: subdistribut

...ms from category B cause responses that fall into a normal distribution. Moreover, both A and B give rise to the same "upper" distribution, but A also gives rise to an extra lower distribution. For any given A item and any given subject, the response might 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 f...

> 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 +

...test and its data-driven version - surv2.ks: Kolmogorov?Smirnov, Cram?r?von Mises and Anderson?Darling test * comparison of two cumulative incidence functions for competing risks data - cif: estimation and plotting of cumulative incidence functions - cif2.logrank: logrank-type test for subdistribution hazards - cif2.neyman: Neyman's smooth test and its data-driven version - cif2.ks: Kolmogorov?Smirnov test - cif2.int: integrated-difference test * goodness of fit tests of the proportional rate assumption (proportional hazards or proportional odds functions in two samples) -...

...test and its data-driven version - surv2.ks: Kolmogorov?Smirnov, Cram?r?von Mises and Anderson?Darling test * comparison of two cumulative incidence functions for competing risks data - cif: estimation and plotting of cumulative incidence functions - cif2.logrank: logrank-type test for subdistribution hazards - cif2.neyman: Neyman's smooth test and its data-driven version - cif2.ks: Kolmogorov?Smirnov test - cif2.int: integrated-difference test * goodness of fit tests of the proportional rate assumption (proportional hazards or proportional odds functions in two samples) -...

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 achieve...

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. With censored data you would have some insects whose endpoint had not been observed, e.g., it's time to write the paper and some of the durn things have neither emerged nor died yet....

...don't understand in your question. Is treatment a time-dependent covariate? That is, do patients receive the treatment at the beginning of the study or later? cmprsk cannot handle time-dependent covariates. But if treatment is a baseline covariate, but has a time-varying effect (i.e. does the subdistribution hazard ratio varies with time?), your solution to assess that is weird, because you will transform your baseline covariate into a time-dependent one, thus considering all the patients to receive no treatment the first year. For sure, the treatment wont have any effect for the first year. To ass...

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):