search for: coxmodel

Hi, if I got it right then the survival-time we expect for a subject is the integral over the specific survival-function of the subject from 0 to t_max. If I have a trained cox-model and want to make a prediction of the survival-time for a new subject I could use survfit(coxmodel, newdata=newSubject) to estimate a new survival-function which I have to integrate thereafter. Actually I thought predict(coxmodel, newSubject) would do this for me, but I?m confused which type I have to declare. If I understand the little pieces of documentation right then none of the availabl...

Dear all, As I said in my previous email I have just upgraded to R 2.14.0 on Windows 7. I have just run the 'coxph' function and notice that a Concordance statistic is produced. Is there any way to extract this information from the output? E.g. can I call the concordance value independently of calling the output? Many thanks, Laura [[alternative HTML version deleted]]

...n a long winded manner due to my lack of programming experience) I then attach T1 T2 and T3 to a dataset KidneyT that contains other variables i wish to use in a Cox regression model. I require the log likelihood statistic of a Cox model when variables T1 T2 T3 and some from KidneyT are fitted. ( coxmodel$loglik[2] ) ######################################################################## ########## I can get this to work fine for specified values of tf1 and tf2, but wish to get the loglikelihood value for many combinations of tf1 and tf2 which are times from 1 to 50 hours i.e I need loglik statis...

...iduals or can/does it take in consideration that it is the history of a single individual? Is this even possible? An example: Individual x has a history of 3 months and the cox model is fitted with two time-varying covariates: a & b testcase <- data.frame(a =[4 5 2], b = [1 0 1]) survest(coxmodel, testcase, time = c(1,2,3)) Is this the right way to compute the probabilities? Thank you in advance! Regards, Laurens Alberts [[alternative HTML version deleted]]

Hi everyone, I'm running a cox ph model on a dataset with a number of variables. Each variable has a different number of missing data, so that coxph() drops the individuals who are missing data at one or more variables. Because of this dropping (totally fine btw) I want to know how many events I am left with in the model. Is there a way of extracting them from the coxph() fit? or in any other

...riables I deem sufficiently important to force into the model regardless of AIC or p value. This is my present log over commands ************ library(rms) library(survival) library(Hmisc) data1 <- read.table("optimism.csv", header=T, sep=",") attach(data1) coxmodel4 <- coxph(formula=Surv(OS,mors) ~ iAJCC2+iAJCC3+iPS2+iPS3++alder_diag+gender+vol_GTV+iforb2+iforb3+hem_LNL+ser o_thromb+LDH_UNL+ALAT_UNL+BASP_UNL+sero_bili+resection_perf+sero_WBC, data=data1, x=TRUE, y=TRUE,method=c("efron")) coxmodel.streg<-step(coxmodel4) I would like to &qu...