similar to: Cox model approximaions (was "comparing SAS and R survival....)

Hello Everyone,   I'm learning how to perform various statistical analyses in R. I'm checking my understanding by replicating examples from my SAS books. Below is an attempt to replicate a Cox Proportional Hazards model with a time-varying covariate. I think I'm doing this correctly but am not completely sure. I would appreciate it if someone could double-check my results. In case

A.S.: I am sorry for sending my first mail to <r-help at R- project.org>. --------------------------------------------------------- Hallo, computing a Cox proportional hazards model in SPSS 9.0 and R 1.2.2 produces different results for beta-coefficient. I use the follwing data set (source: example in help(coxph), somewhat modified) Time Status Covariate (x)

Dear R-help members, Apologies - I am posting on behalf of a colleague, who is a little puzzled as STATA and R seem to be yielding different survival estimates for the same dataset when treating a variable as ordinal. Ordered() is used to represent an ordinal variable) I understand that R's coxph (by default) uses the Efron approximation, whereas STATA uses (by default) the Breslow. but we

Dear R-help, I was comparing SAS (I do not know what version it is) and R (version 2.6.0 (2007-10-03) on Linux) survival analyses with time-dependent covariates. The results differed significantly so I tried to understand on a short example where I went wrong. The following example shows that even when argument 'method' in R function coxph and argument 'ties' in SAS procedure

Dear all, I am struggling with calculation of Schoenfeld residuals of my Cox Ph models. Based on the formula as attached, I calculated the Schoenfeld residuals for both non tied and tied data, respectively. And then I validated my results with R using the same data sets. However, I found that my results for non-tied data was ok but the results for tied data were different from R's. How

I'm trying to estimate a cox proportional hazards regression for repeated events (in gap time) with time varying covariates. The dataset consists of just around 6000 observations (lines) (110 events). The (stylized) data look as follows: unit dur0 dur1 eventn event ongoing x 1 0 1 0 0 0 32.23 1 1 2 0 1 1 35.34 1

Dear all, I was wondering if anyone can help? I am an R user but recently I have resorted to SAS to calculate the probability of the event (and the associated confidence interval) for the Cox model with combinations of risk factors. For example, suppose I have a Cox model with two binary variables, one for gender and one for treatment, I wish to calculate the probability of survival for the

dears, I like two write a program with R to estimate the coefficients of covariate,I like two know the original program for this programing for partial maximum likelihood for cox models with two co variate. I did it with coxph command, thanks [[alternative HTML version deleted]]

Hi, I am computing the Nelson-Aalen (NA) estimate of baseline cumulative hazard in two different ways using the "survival" package. I am expecting that they should be identical. However, they are not. Their difference is a monotonically increasing with time. This difference is probably not large to make any impact in the application, but is annoyingly non-trivial for me to just

Hi All, I'm trying to run a conditional logistic regression in R (2.14.0) using clogit from the survival package. The dataset I have is relatively small (300 observations) with 25 matched strata- there are roughly 2 controls for each case, and some strata have multiple case/control groups. When I try to fit a very simple model with a binary outcome and a single continuous exposure R seems to

Dear R-users, I am checking the proportional hazard assumption of a cox model for a given covariate, let say Z1, after adjusting for other relavent covariates in the model. To this end, I fitted cox model stratified on the discrete values of Z1 and try to get beslow estimator for the baseline cumulative hazard function (H(t)) in each stratum. As far as i know, if the proportionality assumption

A few days ago I uploaded to CRAN a new package called 'eha', which stands for 'Event History Analysis'. Its main focus is on proportional hazards modeling in survival analysis, and in that respect eha can be regarded as a complement and an extension to the 'survival' package. In fact eha requires survival. Eha contains three functions for proportional hazards

A few days ago I uploaded to CRAN a new package called 'eha', which stands for 'Event History Analysis'. Its main focus is on proportional hazards modeling in survival analysis, and in that respect eha can be regarded as a complement and an extension to the 'survival' package. In fact eha requires survival. Eha contains three functions for proportional hazards

The following code works in Splus but not in R coxph <- function(formula, data, weights, subset, na.action, init, control, method= c("efron", "breslow", "exact"), singular.ok =TRUE, robust=FALSE, model=FALSE, x=FALSE, y=TRUE, ...) { method <- match.arg(method) Call <- match.call() # create a call to model.frame() that contains the formula

I have a dataset with event=death time (from medical examination until death/censoring) dose (given at examination time) Two groups are considered, a non-exposed group (dose=0), an exposed group (dose between 5 and 60). For some reason there is a theory of the dose increasing its effect over time (however it was only given (and measured) once = at the time of examination). I tested a model:

This is a simple R program that I have been trying to run. I keep running into the "singular matrix" error. I end up with no sensible results. Can anyone suggest any changes or a way around this? I am a total rookie when working with R. Thanks, Rasika > library(survival) Loading required package: splines > args(coxph) function (formula, data, weights, subset, na.action, init,

Dear R Help list, My question is regarding extracting the standard error or Z-score from a cph or coxph call. My Cox model is: - modz=cph(Surv(TSURV,STATUS)~RAGE+DAGE+REG_WTIME_M+CLD_ISCH+POLY_VS, data=kidneyT,method="breslow", x=T, y=T) I've used names(modz) but can't see anything that will let me extract the Z scores for each coefficient or the standard errors in the same

I should hope that there is trouble, since "type III" is an undefined concept for a Cox model. Since SAS Inc fostered the cult of type III they have recently added it as an option for phreg, but I am not able to find any hints in the phreg documentation of what exactly they are doing when you invoke it. If you can unearth this information, then I will be happy to tell you whether

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

Hello, I am currently trying to simulate data and analyze it using the frailty option in the coxph function. I am working with recurrent event data, using counting process notation. Occasionally, (about 1 in every 100 simulations) I get the following warning: Error in coxph(Surv(start, end, censorind) ~ binary + uniform + frailty(subject, : X matrix deemed to be singular; variable 2 My