search for: allignol

Dear all, A new task view on survival analysis is now online. It attempts to deal with all the R-packages that permit to analyze time-to-event data. Any comments or suggestions to improve the task view are very welcome. Best regards, Arthur Allignol Freiburg Center for Data Analysis and Modeling, Freiburg University, Germany

Hola Carlos. Muchas gracias. No es exactamente lo que estoy buscando (sería genial ver que alguien tiene un paquete con la prueba de Nam-D'Agostino) pero puedo aprovechar algo de código. Encontré alguna referencia al test de Gronnesby&Borgan en el paquete stcoxgof de Stata, pero estoy torpe para encontrar algo hecho en R (y me extraña que no haya nada) Un saludo, Miguel. De: Carlos

...random 0 0 Call: comp.risk(Surv(relt, rels > 0) ~ random, rel, rel$rels, times[-1], causeS = 1, resample.iid = 1, model = "prop") any help is very welcome regards Philippe G ----- Message d'origine ---- De : Arthur Allignol <arthur.allignol@fdm.uni-freiburg.de> À : Philippe Guardiola <allogreffe@yahoo.fr> Cc : R-help@r-project.org; phguardiol@aol.com Envoyé le : Vendredi, 22 Août 2008, 11h53mn 42s Objet : Re: [R] Help on competing risk package cmprsk with time dependent covariate Hello, Something i don...

A lo mejor si escribes a los que mantienen la Task-View de R (Survival Analysius) te pueden dar la clave...: CRAN Task View: Survival Analysis*Maintainer:*Arthur Allignol and Aurelien Latouche*Contact:*arthur.allignol at fdm.uni-freiburg.de*Version:*2013-08-09 Y en Stata, como referencia adicional (que puede conducir a algo equivalente en R): http://www.jstatsoft.org/v53/i12/paper Saludos, Carlos Ortega www.qualityexcellence.es El 4 de octubre de 2013 14:32,...

Dear friends, I have used R for some time now and have a tricky question about the coxph-function: To sum it up, I am not sure whether I can use coxph in conjunction with missing covariate data in a model with time-variant covariates. The point is: I know how "old" every piece that I oberserve is, but do not have fully historical information about the corresponding covariates. Maybe you

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Dear R-users Has anybody implemented a function/package that will compute an individual's risk of an event in the presence of competing risks, adjusted for the individual's covariates? The only thing that seems to come close is the cuminc function from cmprsk package, but I would like to adjust for more than one covariate (it allows you to stratify by a single grouping vector). Any

Guys, Is anyone using snowfall? It seems that the last version is broken. sfinit contains test code: data("config", package = "snowfall") configM <- as.matrix(t(config)) config <- as.list(configM) names(config) <- dimnames(configM)[[2]] .sfOption$SERVER <<- as.character(config[["SERVER"]]) .sfOption$PORT <<-

Dear R users I would like to calculate the Cumulative incidence for an event adjusting for competing risks and adjusting for covariates. One way to do this in R is to use the cmprsk package, function crr. This uses the Fine & Gray regression model. However, a simpler and more classical approach would be to implement the Kalbfleisch & Prentice method (1980, p 169), where one fits cause

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 ?).