similar to: frailtypack: new options !

Dear All, We are happy to announce, after a long gestation, the release of the new version of FRAILTYPACK (version 2.2-9.5) which is now available from CRAN. The package fit general frailty models using penalized likelihood estimation, for clustered or recurrent events. For instance : -- ADDITIVE FRAILTY MODELS for proportional hazards models with two correlated random effects (intercept

Dear All, We are happy to announce, after a long gestation, the release of the new version of FRAILTYPACK (version 2.2-9.5) which is now available from CRAN. The package fit general frailty models using penalized likelihood estimation, for clustered or recurrent events. For instance : -- ADDITIVE FRAILTY MODELS for proportional hazards models with two correlated random effects (intercept

Dear R users, We are pleased to tell you that "FRAILTYPACK" has been updated. "FRAILTYPACK" stands now for general frailty models estimated with a semi-parametrical penalized likelihood, but also with a parametrical approach. In case of comments/corrections/remarks/suggestions -- which are very welcome --please contact the maintainer directly. Kind regards, The

Dear R users, We are pleased to tell you that "FRAILTYPACK" has been updated. "FRAILTYPACK" stands now for general frailty models estimated with a semi-parametrical penalized likelihood, but also with a parametrical approach. In case of comments/corrections/remarks/suggestions -- which are very welcome --please contact the maintainer directly. Kind regards, The

Dear List, In documents (Therneau, 2003 : On mixed-effect cox models, ...), as far as I came to know, coxme penalize the partial likelihood (Ripatti, Palmgren, 2000) where as frailtyPenal (in frailtypack package) uses the penalized the full likelihood approach (Rondeau et al, 2003). How, then, coxme and coxph(...frailty(id, dist='gauss')) differs? Just the coding algorithm, or in

Dear, I want to analyze two-level survival data using a shared frailty model, for which I want to use the R package 'Frailtypack", proposed by Rondeau et al. The dataset was built using SAS software. I also tried to change the format using SPSS and Excell. My (reduced) dataset has following column names: ID entry time status family var1 I used following command: >

I can't comment on frailtypack issues, but would like to mention that coxme will handle nested models, contrary to the statement below that "frailtypack is perhaps the only .... for nested survival data". To reprise the original post's model cgd.nfm <- coxme(Surv(Tstart, Tstop, Status) ~ Treatment + (1 | Center/ID), data=cgd.ag) And a note to the poster-- you should

Assume that we collect below data : - subjects = 20 males + 20 females, every single individual is independence, and difference events = 1, 2, 3... n covariates = 4 blood types A, B, AB, O http://r.789695.n4.nabble.com/file/n4245397/CodeCogsEqn.jpeg ?m = hazards rates for male ?n = hazards rates for female Wm = Wn x ?, frailty for males, where ? is the edge ratio of male compare to female Wn =

Hi everyone I am attempting to estimate a model with a frailty effect distributed as a lognormal variable.I am using the following code: frailtyPenal(formula, data, ..., RandDist = "LogN") I get the following error message: Error in frailtyPenal(Surv(,) ~ + , : unused argument(s) (RandDist = "") What can I do? Thanks for the help [[alternative HTML version deleted]]

Hi, when i run the following code i get this massege: "The instruction at 0x######## reference memory at 0x#######, the memory cannot be "read". and then i have to close R. what is the problem and how can i solve it? thanks in advance Avi my code # frailtypack library(frailtypack) cgd.ag <- read.csv("C:/rfiles/RE/cgd.csv") cgd.nfm <-frailtyPenal(Surv(TStart,

On Tue, 6 May 2003, Jerome Asselin wrote: > > I am confused on how the log-likelihood is calculated in a parametric > survival problem with frailty. I see a contradiction in the frailty() help > file vs. the source code of frailty.gamma(), frailty.gaussian() and > frailty.t(). > > The function frailty.gaussian() appears to calculate the penalty as the > negative

Hello I would like to compare the results obtained with a classical non parametric proportionnal hazard model with a parametric proportionnal hazard model using a Weibull. How can we obtain the equivalence of the parameters using coxph(non parametric model) and survreg(parametric model) ? Thanks Virginie

I am confused on how the log-likelihood is calculated in a parametric survival problem with frailty. I see a contradiction in the frailty() help file vs. the source code of frailty.gamma(), frailty.gaussian() and frailty.t(). The function frailty.gaussian() appears to calculate the penalty as the negative log-density of independent Gaussian variables, as one would expect: >

Dear R-list users, I am trying to do simulation of survival data to enable it to run under frailty option. Below is the function a that I am using. My questions are: 1. How do I modify it to get bigger (hopefully significant) value of Variance of random effect? 2. What changes do I have to make in the function to run it under correlated frailty model? (may be in kinship package) 3. Is there

Dear all, I'm new to frailty model. I have a question on the output from 'survival' pack. Below is the output. What does gamma1,2,3 refer to? How do I calculate joint hazard function or marginal hazard function using info below? Many thanks! Call: coxph(formula = surv ~ as.factor(tibia) + frailty(as.factor(bdcat)), data = try) n=877 (1 observation deleted due to missingness)

Dear R-help, I am trying to estimate a Cox model with nested effects, or better h(t,v,w)=v*w*h0(t)*exp(B'x) where h(t,v,w) is the individual hazard function w and v are both frailty terms (gamma or normal distributed) I have 12 clusters and for each one of them I would like to associate a realization of v, while w is a random effect for the whole population. At the population level

Dear all, I would have some questions on the coxph function for survival analysis, which I use with frailty terms. My model is: mdcox<-coxph(Surv(time,censor)~ gender + age + frailty(area, dist='gauss'), data) I have a very large proportion of censored observations. - If I understand correctly, the function mdcox$frail will return the random effect estimated for each group on the

SEE ALSO ORIGINAL POSTING IN PR#2933 On May 6, 2003 03:58 pm, Thomas Lumley wrote: > > Looking at a wider context in the code > > pfun <- function(coef, theta, ndeath) { > if (theta == 0) > list(recenter = 0, penalty = 0, flag = TRUE) > else { > recenter <- log(mean(exp(coef))) > coef <- coef - recenter

I am trying to build a model to aid a clinical decision. Certain patients have a blood marker measured at each visit - a rise of this may indicate recurrence of the cancer after treatment (endpoint is "clinical recurrence", censored). In a proportion (up to 30%), this rise is a false positive - hence I wish to correlate factors at the time of the rising test to clinical recurrence,

Hello, I am currently trying to simulate data and analyze it using the frailty option in the coxph function. I am working with recurrent event data, using counting process notation. Occasionally, (about 1 in every 100 simulations) I get the following warning: Error in coxph(Surv(start, end, censorind) ~ binary + uniform + frailty(subject, : X matrix deemed to be singular; variable 2 My