similar to: Hazard curves

Hi All, This sounds a relatively simple query, and I hope it is! I am looking at a continuous variable, age. I am looking at time to 12-month remission and can calculate the HR and 95% confidence interval are follows: coxfita = coxph(Surv(rem.Remtime,rem.Rcens)~nearma$all.age,data=nearma) exp(coxfita$coefficients) exp(confint(coxfita)) However, because I am looking at age as a continuous

Dear All, Sorry to bother you again. I have a model: coxfita=coxph(Surv(rem.Remtime/365,rem.Rcens)~all.sex,data=nearma) and I'm trying to do a plot of Schoenfeld residuals using the code: plot(cox.zph(coxfita)) abline(h=0,lty=3) The error message I get is: Error in plot.window(...) : need finite 'ylim' values In addition: Warning messages: 1: In sqrt(x$var[i, i] * seval) : NaNs

Hi all, I'm very confused! I've been using the same code for many weeks without any bother for various covariates. I'm now looking at another covaraite and whenever I run the code you can see below I get an error message: "Error in rep(0, nrow(data)) : invalid 'times' argument" This code works: # remove 'missing' cases from data # snearma <-

Dear All, I am attempting to use forward and/or backward selection to determine the best model for the variables I have. Unfortunately, because I am dealing with patients and every patient is receiving treatment I need to force the variable for treatment into the model. Is there a way to do this using R? (Additionally, the model is stratified by randomisation period). I know that SAS can be

Dear R-Help, I am using the 'mfp' package. It produces three plots (as I am using the Cox model) simultaneously which can be viewed together using the following code: fit <- mfp(Surv(rem.Remtime,rem.Rcens)~fp(age)+strata(rpa),family=cox,data=nearma,select=0.05,verbose=TRUE) par(mfrow=c(2,2)) plot(fit) They can be viewed separately but the return key must be pressed after each graph

Dear All, I am contemplating centering the covariates in my Cox model to reduce multicollinearity between the predictors and the interaction term and to render a more meaningful interpretation of the regression coefficient. Suppose I have two indicator variables, x1 and x2 which represent age categories (x1 is patients less than 16 while x2 is for patients older than 65). If I use the following

Dear R-help, I am using R 2.14.1 on Windows 7 with the 'gfcure' package (cure rate model). I have included the treatment variable in the cure part of the model as shown below: Ø ref_treat <- gfcure(Surv(rem.Remtime,rem.Rcens)~1,~1+strata(drpa)+factor(treat(delcure)),data=delcure,dist="loglogistic") >From that I can obtain the coefficients, standard errors etc as per

Dear all, I am using Windows and R 2.9.2 for my analyses. I have a large dataset and I am particularly interested in looking at time to an event for a continuous variable. I would like to produce a plot of log(relative risk) or relative risk (also known as hazard ratio) against the continuous variable. I have spent a long time looking for advice on how to do this but my search has proved

Dear All, I have analysed time to event data for continuous variables by considering the multivariable fractional polynomial (MFP) model and comparing this to the untransformed and log transformed model to determine which transformation, if any, is best. This was possible as the Cox model was the underlying model. However, I am now at the situation where the assumption that the competing risks

Hi, at my local university network, I have to make sure no student uploads more than x GB/day. So far, I give them unlimited bandwidth until they have more than y GB (y<x) upload. After that, I use the u32 filter to associate the affected IP address with a HTB class limited to the rate (remainingtraffic/remainingtime). Since the accounting is done with ipt_ACCOUNT right now and the netfilter

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Hello, I have a problem regarding calculation of Cumulative Incidence Function. The event of interest is failure of bone-marrow transplantation, which may occur due to relapse or death in remission. The data set that I have consists of- lifetime variable, two indicator variables-one for relapse and one for death in remission, and the other variables are donor type (having 3 categories), disease

Dear all, I would like to draw three forest plots to represent results at years 1, 2 and 3. I have the data as point estimates and 95% confidence intervals. Using the following code I can get three basic forest plots - the first which has the table of results. I have to plot each separately as the usual par(mfrow=c(3,1)) does not work with the function forestplot within rmeta. I can easily put

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

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,

Hi all, Is there an equivalent to predict(...,type="linear") of a Proportional hazard model for a Cox model instead? For example, the Figure 13.12 in MASS (p384) is produced by: (aids.ps <- survreg(Surv(survtime + 0.9, status) ~ state + T.categ + pspline(age, df=6), data = Aidsp)) zz <- predict(aids.ps, data.frame(state = factor(rep("NSW", 83), levels =

I am trying to code a steepest ascent algorithm to optimize parameters used in a survivor function type problem. My unknown parameters (alpha, Beta0, and Beta1) for which I have been able to optimize using Newton's method. I keep getting an error because my alpha becomes negative and I can't calculate the likelihood. Here is my log likelihood I am optimizing (in LaTex): l=\sum _{ i=1 }^{

Hi Duncan, when I run "opt -std-compile-opts" on the original source code it has the same output of O3. when I run "opt -std-compile-opts" on the -O3 optimized code, things get even more weird, it outputs the following code: while.body: ; preds = %while.body, %entry %indvar = phi i32 [ 0, %entry ], [ %indvar.next.3, %while.body ] %tmp

After a few more tests, I found out that if we set -unroll-threshold to a value large enough, and run "opt -std-compile-opts" or "opt -O3" 3 times, the unroll will be able to unroll the original loop 32 times, and when you have it unrolled for at least 32 times a optimization is triggered, folding it to a single "%xor.3.3.1 = xor i32 %tmp6, -1" (dont know why it does

I like the new difftime functionality. Here's a dataframe of 5k run times: > r5k race date totaltime pace mile 1 RUDOLPH 2004-12-03 19:00:00 27.76667 mins 8.937224 mins 3.106856 2 RUDOLPH 2005-12-02 18:30:00 25.28333 mins 8.137916 mins 3.106856 3 FROSTBITE 2005-12-10 07:00:00 24.75000 mins 7.966253 mins 3.106856 4 JUDICATA 2006-03-04