Dear Geskus, I want to develop a prediction model. I followed your paper and analysed thro' weighted coxph approach. I can develop nomogram based on the final model also. But I do not know how to do internal validation of the model and subsequently obtain calibration plot. Is it possible to use Wolbers et al Epid 2009 approach 9 (R code for internal validation and calibration) . It is possible to get these measures after using R function 'crr' or 'FGR'. That is why I wanted to go in that route. At the same time, I had this doubt because their approach assume a record per individual whereas weight coxph creates two or more records per individual. I am new to R and could not modify the R code easily. Any suggestion? Has anyone done internal validation and calibration after using weighted coxph approach? Can you kindly refer me to the reference which has R code?
Thank you very much for all your inputs and suggestions Regards Amalraj raja -----Original Message----- From: Ronald Geskus [mailto:statist...@inter.nl.net] Sent: 21 March 2018 04:01 To: r-help@r-project.org Cc: Raja, Dr. Edwin Amalraj <amalraj.r...@abdn.ac.uk> Subject: Re: [R] selectFGR - variable selection in fine gray model for competing risks Dear Raja, A Fine and Gray model can be fitted using the standard coxph function with weights that correct for right censoring and left truncation. Hence I guess any function that allows to perform stepwise regression with coxph should work. See e.g. my article in Biometrics https://doi.org/10.1111/j.1541-0420.2010.01420.x, or the vignette "Multi-state models and competing risks" in the survival package. best regards, Ronald Geskus, PhD head of biostatistics group Oxford University Clinical Research unit Ho Chi Minh city, Vietnam associate professor University of Oxford http://www.oucru.org/dr-ronald-b-geskus/ "Raja, Dr. Edwin Amalraj" <amalraj.r...@abdn.ac.uk> writes: > Dear All, > > I would like to use R function 'selectFGR' of fine gray model in > competing risks model. I used the 'Melanoma' data in 'riskRegression' > package. Some of the variables are factor. I get solution for full > model but not in variable selection model. Any advice how to use > factor variable in 'selectFGR' function. The following R code is > produced below for reproducibility. > > library(riskRegression) > library(pec) > dat <-data(Melanoma,package="riskRegression") > Melanoma$logthick <- log(Melanoma$thick) > f1 <- Hist(time,status)~age+sex+epicel+ulcer > df1 <-FGR(f1,cause=1, data=Melanoma) > df1 > df <-selectFGR(f1, data=Melanoma, rule ="BIC", direction="backward") > > Thanks in advice for your suggestion. Is there any alternative solution ? > > Regards > Amalraj raja > > > The University of Aberdeen is a charity registered in Scotland, No SC013683. > Tha Oilthigh Obar Dheathain na charthannas clàraichte ann an Alba, Àir. SC013683. The University of Aberdeen is a charity registered in Scotland, No SC013683. Tha Oilthigh Obar Dheathain na charthannas clàraichte ann an Alba, Àir. SC013683. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
