similar to: coxph and drop1

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

Hello All, Am having some trouble computing Type III SS in a Cox Regression using either drop1 or Anova from the car package. Am hoping that people will take a look to see if they can tell what's going on. Here is my R code: cox3grp <- subset(survData, Treatment %in% c("DC", "DA", "DO"), c("PTNO", "Treatment", "PFS_CENSORED",

Dear all, I'm struggling with predicting "expected time until death" for a coxph and survreg model. I have two datasets. Dataset 1 includes a certain number of people for which I know a vector of covariates (age, gender, etc.) and their event times (i.e., I know whether they have died and when if death occurred prior to the end of the observation period). Dataset 2 includes another

I tried to compare if cch() and coxph() can generate same result for same case cohort data Use the standard data in cch(): nwtco Since in cch contains the cohort size=4028, while ccoh.data size =1154 after selection, but coxph does not contain info of cohort size=4028. The rough estimate between coxph() and cch() is same, but the lower and upper CI and P-value are a little different. Can we

Thanks for the reproducable example. I can confirm that it fails on my machine using survival 2-37.5, the next soon-to-be-released version, The issue is with NextMethod, and my assumption that the called routine inherited everything from the parent, including the environment chain. A simple test this AM showed me that the assumption is false. It might have been true for Splus. Working this

I have a model with quant vars only and the error message does not make sense: (mod1 <- coxph(Surv(time=strt,time2=stp,event=(resp==1))~ +incpost+I(amt/1e5)+rate+strata(termfac), subset=dt<"2010-08-30", data=inc,method="efron")) Call: coxph(formula = Surv(time = strt, time2 = stp, event = (resp == 1)) ~ +incpost + I(amt/1e+05) + rate + strata(termfac),

Hello, I would like to compare two different models in the framework of Cox proportional hazards regression models. On Rsitesearch and google I don't find a clear answer to my question. My R-Code (R version 2.9.0) coxph.fit0 <- coxph(y ~ z2_ + cluster(as.factor(keys))+ strata(stratvar_), method="breslow" ,robust=T ) coxph.fit1 <- coxph(y ~ z_ +

I've never seen this, and have no idea how to reproduce it. For resloution you are going to have to give me a working example of the failure. Also, per the posting guide, what is your sessionInfo()? Terry Therneau On 08/09/2012 04:11 AM, r-help-request at r-project.org wrote: > I have a couple of questions with regards to fitting a coxph model to a data > set in R: > > I have a

This is a follow-up to a query that was posted regarding some problems that emerge when running anova analyses for cox models, posted by Mathias Gondan: Matthias Gondan wrote: >* Dear List,*>**>* I have tried a stratified Cox Regression, it is working fine, except for*>* the "Anova"-Tests:*>**>* Here the commands (should work out of the box):*>**>*

Since I read the list in digest form (and was out ill yesterday) I'm late to the discussion. There are 3 steps for predicting survival, using a Cox model: 1. Fit the data fit <- coxph(Surv(time, status) ~ age + ph.ecog, data=lung) The biggest question to answer here is what covariates you wish to base the prediction on. There is the usual tradeoff between too few (leave out something

Dear List, Can anyone please explain the difference between cluster() and frailty() in a coxph? I am a bit puzzled about it. Would appreciate any useful reference or direction. cheers, Ehsan > marginal.model <- coxph(Surv(time, status) ~ rx + cluster(litter), rats) > frailty.model <- coxph(Surv(time, status) ~ rx + frailty(litter), rats) > marginal.model Call: coxph(formula =

1. The weibull is the only distribution that can be written in both a proportional hazazrds for and an accelerated failure time form. Survreg uses the latter. In an ACF model, we model the time to failure. Positive coefficients are good (longer time to death). In a PH model, we model the death rate. Positive coefficients are bad (higher death rate). You are not the first to be confused

Hi there, I have a question on how to extract the linear term in the penalized spline in coxph. Here is a sample code: n=100 set.seed(1) x=runif(100) f1 = cos(2*pi*x) hazard = exp(f1) T = 0 for (i in 1:100) { T[i] = rexp(1,hazard[i]) } C = runif(n)*4 cen = T<=C y = T*(cen) + C*(1-cen) data.tr=cbind(y,cen,x) fit=coxph(Surv(data.tr[,1],

The coxph function in R is not working for me when I use a continuous predictor in the model. Specifically, it fails to converge, even when bumping up the number of max iterations or setting reasonable initial values. The estimated Hazard ratio from the model is incorrect (verified by an AFT model). I've isolated it to the "x1" variable in the example below, which is log-normally

Dear all, The help file for the generic function vcov states "Classes with methods for this function include: 'lm', 'glm', 'nls', 'lme', 'gls', 'coxph' and 'survreg' (the last two in package 'survival')." Since, I am not able to use vcov.coxph(), I am wondering whether I am missing something (as I suspect..) regards, vito

Dear users, In a study with recurrent events: My objective is to get estimates of survival (obtained through a Cox model) by rank of recurrence and by treatment group. With the following code (corresponding to a model with a global effect of the treatment=rx), I get no error and manage to obtain what I want : data<-(bladder)

Hi All, I'm runnning coxph() on 90 different datasets (in a loop). 1. I'm wondering how can I get "log-likelihood" value from coxph() output. Currently I can only see following: Likelihood ratio test = Wald test = Score (logrank) test = 2. Once I have likelihood value, I would like to extract these from R output (for each data set). Can any one help please, thanks in

Dear R help list, I am modeling some survival data with coxph and survreg (dist='weibull') using package survival. I have 2 problems: 1) I do not understand how to interpret the regression coefficients in the survreg output and it is not clear, for me, from ?survreg.objects how to. Here is an example of the codes that points out my problem: - data is stc1 - the factor is dichotomous

Hi I am estimating the following coxph function with stratification and frailty?where each person had multiple events. m<-coxph(Surv(dtime1,status1)~gender+cage+uplf+strata(enum)+frailty(id),xmodel) ? > head(xmodel) id enum dtime status gender cage uplf 1 1008666 1 2259.1412037 1 MA 0.000 0 2 1008666 2 36.7495023 1 MA 2259.141 0 3 1008666

Dear all, this is probably a very silly question, but could anyone tell me what the different parameters in a coxph model with a frailty.gamma term mean? Specifically I have two questions: (1) Compared to a "normal" coxph model, it seems that I obtain two standard errors [se(coef) and se2]. What is the difference between those? (2) Again compared to a "normal" coxph model,