similar to: Building a web risk calculator based on Cox PH--definitive method for calculating probability?

Here is an example of how to do it. > library(survival) > vfit <- coxph(Surv(time, status) ~ celltype + trt, data=veteran) > userinput <- data.frame(celltype="smallcell", trt = 1) > usercurve <- survfit(vfit, newdata=userinput) #the entire predicted survival curve > user2 <- summary(usercurve, time= 2*365.25) # 2 year time point > user2$surv [1]

Dear Dr Hornik / R help list, I am using the mda package and in particular the fda routine to classify a set of 162 neurons falling in to 11 neuronal cell types according to 17 morphological variables. I would like to use a cross-validation approach in which I split the data, train with one part amd then test the predictive accuracy of the discriminant functions with the remaining part. However

Hello R-help. I am trying to make a stacked barplot where the color of the sections of each bar depend on another variable. > myData[1:11,] score percent marker cellType Malignant 1 0 100.00000 ESR1 (ER) Bladder.M(5) TRUE 2 0 80.00000 PAX8 Bladder.M(5) TRUE 3 1 20.00000 PAX8 Bladder.M(5) TRUE 4 0 100.00000 ESR1 (ER) Brain.N(3) FALSE 5 0

Dear R-helpers I have a problem related to the use of NLME I think is simply a matter of getting the nlme coding correct, but i cannot get my brain around it I am analysing some 24 growth curves of some cells , and i wanted to say that there are significant differences between the curves in two parameters that describe the pattern of growth. these parameters are from a logistic (r & k)

Hi, I am new to model selection by coefficient shrinkage method such as lasso. And I became particularly interested in variable selection in Cox regression by lasso. I became aware of the coxpath() in R package glmpath does lasso on Cox model. I have tried the sample script on the help page of coxpath(), but I have difficult time understanding the output. Therefore, I would greatly appreciate if

Hello R-help group, I have a question about merging lists. I have two lists: Genes list (hSgenes) name chr strand start end transStart transEnd symbol description feature ENSG00000223972 1 1 11874 14412 11874 14412 DEAD/H box polypeptide 11 like 1DEAD/H box polypeptide 11 like 3DEAD/H box polypeptide 11 like 9 ;;

Hi: I have made a lot of progress reading and manipulating large data files, thanks to the help of several of you. I am now stuck with writing the final file with the following error (see also the full transcript below): Error in as.matrix.data.frame(x) : length of dimnames[2] not equal to array extent I do not know what it means and could not find how to get around that in the manual nor

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 ?).

Dear R users, is there a way, I mean a package, to perform a competing risk model which can handle time dependent covariates ? my main covariate (additional treatment to patients) appears not to follow the proportional hazards assumption, its effect being observed after one year of treatment but not before (this is expected / makes sense on a clinical point of view). SO I was planning to use a

This message was also sent to the MEDSTATS mailing list, so here is the reply I posted to that: Philippe, The machinery to use is to split follow-up time so finely that you can safely assume that rates are constant in each interval, and then just stuff it all into a Poisson model. This allows you to use any kind of time-dependent variables as well as accommodating competing risks. In the Epi

Hello everyone, I am a very new user of R and I have a query about the cuminc function in the package cmprsk. In particular I would like to verify that I am interpreting the output correctly when we have a stratification variable. Hypothetical example: group : fair hair, dark hair fstatus: 1=Relapse, 2=TRM, 0=censored strata: sex (M or F) Our data would be split into: Fair, male,

Hello everyone,   I am a very new user of R and I have a query about the cuminc function in the package cmprsk. In particular I would like to verify that I am interpreting the output correctly when we have a stratification variable.   Hypothetical example:   group : fair hair, dark hair fstatus: 1=Relapse, 2=TRM, 0=censored strata: sex (M or F)   Our data would be split into:   Fair, male,

Hi, after standardizing my data by dividing each covariate by its mean, the hazard ratio of variables ranges btw ~ 0.5 and 3.55. when i test the PH assumption by cox.zph, rho ranges btw -0.4235863 and 0.359827. visualizing the PH assumption by plot, the plot of scaled schoefeld residuals against transformed time doesn't give a line with slope 0 for all covariates. note that there are 100

Hello, I want to generate data set from Cox PH model with gamma frailty effects. theta(parameter for frailty distribution)=2 beta=1.5 n=300 cluster size=30 number of clusters=10 I think I should first generate u from Gamma(Theta,theta) and then using this theta I could not decide how I should generate the survival times? Is there any package for this? or any document you could suggest? Any

From: Peter Oberparleiter <peter.oberparleiter at de.ibm.com>, Heiko Carstens <heiko.carstens at de.ibm.com> Change s390's sysstub.ph so it can also handle syscalls with six parameters. Signed-off-by: Peter Oberparleiter <peter.oberparleiter at de.ibm.com> Signed-off-by: Heiko Carstens <heiko.carstens at de.ibm.com> --- usr/klibc/arch/s390/sysstub.ph | 73

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Dear all, Our institute has an open position for a Ph.D. student on modeling gene network in stem cells. here is the official announcement: --- The Institute for Biomedical Engineering, Department of Cell Biology, RWTH Aachen University Medical School and Helmholtz Institute for Biomedical Engineering, RWTH Aachen University invites applications of highly motivated individuals for a PhD

I'm doing prediction of the survival cases using gene expression profiles(Affymetrix chips). Can somebody tell me how to use the Cox PH model to select genes and make a prediction of survival? Thanks. Guangchun