similar to: Confidence interval of Survival Curve of Weighted Cox Regression

Hi! I am having a few problems with coxph function, I had the same problem with the use of survfit. Here it is: when calling 'M22<-coxphw(Surv(V1,V2,V4)~V5, data=XTDV, id=XTDV$V3, * prentice*= ~V5, robust=TRUE, censcorr=TRUE)' knowning that my data is: > XTDV[1:10,1:3] V1 V2 V3 V4 V5 1 0 36.39706 36 1 343.2224 2 0 36.39706 943 0 161.5931 3 0 36.39706

Hi- I'm at my wit's end trying to get a Grandstream BT-101 POS to register on my * server. Asterisk version is 1.2.1. GS Firmware is rev 1.0.6.7. Basically, I've setup the phone following the instructions at voip-info.org, and it registers for about 10 seconds, then after receiving the SIP NOTIFY from the * server, goes into "flashing display" mode, which indicates some

Hi there, I have a predictor varible "class" which is a categorical variable and a ' coxph' is used to find the coeffients. How can I plot the predicted survival proportion based on this model? Thanks Lisa Wang Princess Margaret Hospital Toronto tel 416 946 4501

Hello, I want to make a fuzzy classification from a dissimilarity matrix (calculated with daisy from package 'cluster'). I have tried to use fanny (package cluster) but I have the same problems than described in a previous message (http://tolstoy.newcastle.edu.au/R/help/05/05/4546.html) i.e. it always gives me two clusters in the results (even if k is different from 2) with the same

Hello and Thanks in Advance, I've been at this for three days now and can't think of anything else to try. Been through the docs and #samba on openprojects and even begged for direction in #samba-technical who suggested writing here. On the ancient RedHat 5.2 server (SAMBA 1.9?) both boxes work OK and continue to work OK with both servers now up (new one is from Mandrake 8.2 - SAMBA

Hi, I'm a PhD student and I want to use the function 'daisy' from the package 'cluster' to compute dissimilarities. My variables are of mixed types so I use the argument 'stand' in daisy to define the type of my variables. I have the following error message : Warning message: binary variable(s) 13, 16, 17, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,

Hello, I'm looking for a R function proceeding MRPP (Multi-Response Permutation Procedures). Is it available? Thanking you in anticipation, Jeanne Vallet, PhD student [[alternative HTML version deleted]]

I am trying to use the survfit() function with the newdata argument to produce predicted survivor curves for a particular covariate profile. The main purpose of the plot will be to visualise the effect of snp1, coded 0 and 1. In my Cox model I have stratified by one variable, edu, and so I know I will automatically get a separate curve for each strata. My problem is how to deal with the

Hello R users Would somebody know how to estimate survival from a frailty cox model, using the function survfit and the argument newdata ? (or from any other way that could provide individual expected survival with standard error); Is the problem related to how the random term is included in newdata ? kfitm1 <- coxph(Surv(time,status) ~ age + sex + disease + frailty(id,

The combination of survfit, coxph, and factors is getting confused. It is not smart enough to match a new data frame that contains a numeric for sitenew to a fit that contained that variable as a factor. (Perhaps it should be smart enough to at least die gracefully -- but it's not). The simple solution is to not use factors. site1 <- 1*(coxsnps$sitenew==1) site2 <-

Dear all, Let's assume I have a clinical trial with two treatments and a time to event outcome. I am trying to fit a Cox model with a time dependent treatment effect and then plot the predicted survival curve for one treatment (or both). library(survival) test <- list(time=runif(100,0,10),event=sample(0:1,100,replace=T),trmt=sample(0:1,100,replace=T)) model1 <- coxph(Surv(time,

Hello, I'm looking to calculate a 95% confidence interval about my estimate for a sample's weighted mean, where the calculated confidence interval would equal the t-test confidence interval of the sample in the case when all of the weights are equal. My initial thought was to simply implement a modified version of the t-test function but substituting the weighted variance and mean for the

Hello, Excuse me for a more methodological than technical question. I'm developing a Cox model with 10 covariates. One of them is age (named "eta"). I've checked proportionality with cox.zph with age continuous: > cox.zph(coxph(Surv(TTP,CENSOTTP)~eta)) rho chisq p eta -0.0154 0.0225 0.88 and categorical (eta<60): >

Dear Members, I required to score probability of survival before specified time using fitted cox model on scoring dataset. On the training sample data I am able to get the probability of a survival before time point(t), but on the scoring dataset, which will have only predictor information I am facing some issues. It would be great help for me if you tell me where am I going wrong! Here is the

I am calculating cox propotional hazards models with the coxph function from the survival package. My data relates to failure of various types of endovascular interventions. I can successfully obtain the LR, Wald, and Score test p-values from the coxph.object, as well as the hazard ratio as follows: formula.obj = Surv(days, status) ~ type coxph.model = coxph(formula.obj, df) fit =

Hello all, thanks for your time and patience. I'm looking for a method in R to analyse the following data: Time to waking after anaesthetic for medical procedures repeated on the same individual. > str(mysurv) labelled [1:740, 1:2] 20 20 15 20 30+ 40+ 50 30 15 10 ... - attr(*, "dimnames")=List of 2 ..$ : NULL ..$ : chr [1:2] "time" "status" -

Hello, I drew a plot of weight and height of people and fitted it with a polynominal regression x^2. (using curve()) Now I would like to draw the confidence interval line for the fitted curve. Please kindly advise the code for the purpose. Thank you. Elaine [[alternative HTML version deleted]]

Dear All, Is it possible to get confidence intervals for Harrell's concordance index or, equivalently, Somer's D using the rms package or in some other way ? I have survival data it would be the c-index in the Cox model setting Many thanks Dr Bernard North Statistical Consultant Statistical Advisory Service Advice and Courses on Research Design and Methodology Imperial College South

Hi all, I just uploaded a new version of the bpcp package. It calculates confidence intervals for a survival distribution for right-censored data using the newly developed beta product confidence procedure. Previously developed methods can have substantial error rate inflation for the lower limit, especially at the right end of the curves when there are small numbers of events. The bpcp method

I wonder if anyone know why survest (a function in Design package) and standard survfit.coxph (survival) returned different confidence intervals on survival probability estimation (say 5 year). I am trying to estimate the 5-year survival probability on a continuous predictor (e.g. Age in this case). Here is what I did based on an example in "help cph". The 95% confidence intervals