search for: survived

Hi all, May i know does R has packages or code to run "Bayesian Relative Survival Analysis"? I have look through Bayesian Survival Analysis(2001) by Joseph George Ibrahim<http://www.google.com/search?tbo=p&tbm=bks&q=inauthor:%22Joseph+George+Ibrahim%22>, Ming-Hui Chen<http://www.google.com/search?tbo=p&tbm=bks&q=inauthor:%22Ming-Hui+Chen%22>, Debajyoti

On 04/08/2009 7:33 PM, Steven McKinney wrote: >> -----Original Message----- >> From: Duncan Murdoch [mailto:murdoch at stats.uwo.ca] >> Sent: Tuesday, August 04, 2009 8:03 AM >> To: Steven McKinney >> Cc: r-devel at stat.math.ethz.ch; R-bugs at r-project.org >> Subject: Re: [Rd] Wishlist: Navigate to "Index" page of help when no >> topic

Dear all, I am confused with the output of survfit.coxph. Someone said that the survival given by summary(survfit.coxph) is the baseline survival S_0, but some said that is the survival S=S_0^exp{beta*x}. Which one is correct? By the way, if I use "newdata=" in the survfit, does that mean the survival is estimated by the value of covariates in the new data frame? Thank you very much!

Hello. I apologize in advance for the VERY lengthy e-mail. I endeavor to include enough detail. I have a question about survival curves I have been battling off and on for a few months. No one local seems to be able to help, so I turn here. The issue seems to either be how R calculates Kaplan-Meier Plots, or something with the underlying statistic itself that I am misunderstanding. Basically,

Dear R users, i try to calculate the probabilty to survive a given time by using the estimated survival curve by kaplan meier. What is the right way to do that? as far as is see i cannot use the predict-methods from the survival package? library(survival) set.seed(1) time <- cumsum(rexp(1000)/10) status <- rbinom(1000, 1, 0.5) ## kaplan meier estimates fit <- survfit(Surv(time,

Hi all, When I install a new package, and don't yet know any function names, I have to play the "poor man's game" to get to the standard help system "Index" page for the package: Poor Man's Game -=20 Load new package;=20 issue search() command; find position (say N) of loaded package;=20 issue objects(pos =3D N) command; get name of a random function (san

Dear R-help, I am using R 2.14.1 on Windows 7. I would like to produce a plot like the attached - although simplified to actual vs. Predicted survival time with distinguishing marks for censored and observed points. I have a dataset and have fitted a Cox model to it. In an attempt to visualise how accurate the model is it would be ideal if I could plot the actual survival times against the

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

Better would be to use interval censored data. Create your data set so that you have (time1, time2) pairs, each of which describes the interval of time over which the tag was lost. So an animal first captured at time 10 sans tag would be (0,10); with tag at 5 and without at 20 would be (5,20), and last seen with tag at 30 would be (30, NA). Then survit(Surv(time1, time2,

I am confused when trying the function survfit. my question is: what does the survival curve given by plot.survfit mean? is it the survival curve with different covariates at different points? or just the baseline survival curve? for example, I run the following code and get the survival curve #### library(survival) fit<-coxph(Surv(futime,fustat)~resid.ds+rx+ecog.ps,data=ovarian)

I found an issue with the data() command this evening when working on the survival package. 1. I have a lot of data sets in the package, almost all used in at least one vignette, help file, or test.? As a space saving measure, I have bundled many of them together, i.e., the file data/cancer.rda contains 19 data sets, many of them small. The resulting file (using xz compression) is quite a bit

G'day all, probably you have noticed this by now, but I thought I ought to report it. :) My scripts that update the SVN sources for R-patched and R-devel, run `tools/rsync-recommended' (for both) and then install both these versions from scratch failed this morning. Apparently the new version of the recommended package `survival' depends on the recommended package `Matrix', but

Hey, I am just starting to learn R now and I typed in this simple survival program: library(survival) t <- c(10,13,18,19,23,30,36,38,54,56,59,75,93,97,104,107,107,107) c <- c(1,0,0,1,0,1,1,0,0,0,1,1,1,1,0,1,0,0) data <- Surv(t,c) km <- survfit(data) summary(km) Call: survfit(formula = data) but everytime I run it I get this error: Error in

The survival package makes significant use of the "specials" argument of terms(), before calling model.frame; it is part of nearly every modeling function. The reason is that strata argments simply have to be handled differently than other things on the right hand side. Likewise for tt() and cluster(), though those are much less frequent. I now get "bug reports" from the

> -----Original Message----- > From: Duncan Murdoch [mailto:murdoch at stats.uwo.ca] > Sent: Tuesday, August 04, 2009 5:06 PM > To: Steven McKinney > Cc: 'r-devel at stat.math.ethz.ch'; 'R-bugs at r-project.org' > Subject: Re: [Rd] Wishlist: Navigate to "Index" page of help when no > topic specified (PR#13860) >=20 > On 04/08/2009 7:33 PM,

Hi, I am trying to calculate net reclassification improvement (NRI) and Inegrated Discrimination Improvement (IDI) for a survival dataset to compare 2 risk models. It seems that the improveProb() in Hmisc package does this only for binary outcome, while rcorrp.cens() does take survival object, but doesn't output NRI or IDI. Can anyone suggest any other packages that can calculate NRI and IDI

Hi I have been asked to provide Weibull parameters from a paper using Kaplan Meir survival analysis. This is something I am not familiar with. The survival analysis in R works nicely and is the same as commercial software (only the graphs are superior in R). The Weibull does not and produces an error (see below). Any ideas why this error should occur? My approach may be spurious.

Hi, I am wondering if anyone can explain to me if cumulative incidence (CI) is just "1 minus kaplan-Meier survival"? Under what circumstance, you should use cumulative incidence vs KM survival? If the relationship is just CI = 1-survival, then what difference it makes to use one vs. the other? And in R how I can draw a cumulative incidence plot. I know I can make a Kaplan-Meier

Hello, I need some results from the survival analysis of my data that I do not know whether exist in Survival Package or how to obtain if they do: 1. The Mean survival time 2. The standard error of the mean 3. Point and 95% Lower & Upper Confidence Intervals estimates Any help will be greatly appreciated. Cem [[alternative HTML version

Hi! I came across R just a few days ago since I was looking for a toolbox for cox-regression. I?ve read "Cox Proportional-Hazards Regression for Survival Data Appendix to An R and S-PLUS Companion to Applied Regression" from John Fox. As described therein plotting survival-functions works well (plot(survfit(model))). But I?d like to do some manipulation with the survival-functions