similar to: error fitting coxph model

I am trying to fit a large Cox model with many predictors. Because there are many predictors, I would like to use the ridge() function to get penalized ml estimates for all coefficients. The problems are that: 1. When I include a factor (like race) in the ridge() function, dummy variables are not created. The resulting model has a single coefficient for the race variable, and I have

Hi, I have a character vector that contains the names of several objects that I would like to pass to a function (specifically, the ridge function in the survival package, but cbind is a similar example). I've been struggling with how to do this so that the object values get interpreted by the function, rather than the object names. For example, x1 <- 1:4 x2 <- 2:5 x3 <-

I am fitting a coxph model on a large dataset (approx 100,000 patients), and then trying to estimate the survival curves for several new patients based on the coxph object using survfit. When I run coxph I get the coxph object back fairly quickly however when I try to run survfit it does not come back. I am wondering if their is a more efficient way to get predicted survival curves from a coxph

Hi, I'm trying to make a boxplot with the data at the end of the message, and when I try to execute the command >boxplot(Diatoms) (or for any other field instead of "Diatoms") I get the following error message: Error in oldClass(stats) <- cl : adding class "factor" to an invalid object Any advice would be much appreciated. Thanks a lot, Miriam Date

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!

I have what I *think* should be a simple problem in R, and hope someone might be able to help me. I'm working with cancer survival data, and would like to calculate adjusted survival figures based on the age of the patient and the tumour classification. A friendly statistician told me I should use Cox proportional hazards to do this, and I've made some progress with using the

Hi Dear R-users I have a database with 18000 observations and 20 variables. I am running cox regression on five variables and trying to use survfit to plot the survival based on a specific variable without success. Lets say I have the following coxph: >library(survival) >fit <- coxph(Surv(futime, fustat) ~ age + rx, data = ovarian) >fit what I am trying to do is plot a survival

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

Hello! I am using the coxph() function for counting process data. I want to include an intervening event as one of my covariates. In order to do this I have split the relevant observations in my data at the time of intervention. But I have not found any way to "inform" coxph() of the id of these observations. The result of this is that coxph() interprets the split data as

Hi, I am working on a study where we need to predict individual survival curves from a cox-model fit with sampling weights. In both R and Splus the survfit.coxph code starts with if(!is.null((object$call)$weights)) stop("Survfit cannot (yet) compute the result for a weighted model") My question is does anyone have code to get the expected survival curve, or even just the base

Hi, I'm interested in building a Cox PH model for survival modeling, using 2 covariates (x1 and x2). x1 represents a 'baseline' covariate, whereas x2 represents a 'new' covariate, and my goal is to figure out where x2 adds significant predictive information over x1. Ideally, I could get a p-value for doing this. Originally, I thought of doing some kind of likelihood ratio

I am having trouble understanding results I'm getting back from coxph doing a recurrent event analysis. I've included the model below and the summary. In some cases, with minor variations, the Robust variance and Wald tests are significant, but the individual covariates may or may not be significant. My main question is: If Wald and robust tests both take into account the

We occasionally utilize the coxph function in the survival library to fit multinomial logit models. (The breslow method produces the same likelihood function as the multinomial logit). We then utilize the predict function to create summary results for various combinations of covariates. For example:

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

Hello, I was hoping that someone could answer a few questions for me (the background is given below): 1) Can the coxph accept an interaction between a covariate and a frailty term 2) If so, is it possible to a) test the model in which the covariate and the frailty appear as main terms using the penalized likelihood (for gaussian/t frailties) b)augment model 1) by stratifying on the variable that

I would like to be able to construct hazard rates (or unconditional death prob) for many subjects from a given survfit. This will involve adjusting the ( n.event/n.risk) with (coxph object )$linear.predictors I must be having another silly day as I cannot reproduce the linear predictor: fit <- coxph(Surv(futime, fustat) ~ age, data = ovarian) fit$linear.predictors[1] [1] 2.612756

Dear R user, I got a question when using the coxph function. I have 5 covariates x1, x2, x3, x4, x5 and I want to write a function so that when given an indicator, e.g., c(1,3,5), I can fit the model as model=coxph(Surv(time, status)~x1+x3+x5). Any idea to play around the form of formula? Thank you very much! Xing Yuan University of Pittsburgh [[alternative HTML version deleted]]

Dear all, My question is: In the Surv object you have two arguments, "time" and "event". I have two events, namely withdrawn and success. I use no event or status argument in "Surv" because all my objects "die" in my data set. Does coxph function calculate the coefficients correctly when you put no "event" argument into the Surv object?

Hi: I have a few clarification questions about the elements returned by the coxph function used in conjuction with a frailty term. I create the following group variable: group <- NULL group[id<50] <- 1 group[id>=50 & id<100] <- 2 group[id>=100 & id<150] <- 3 group[id>=150 & id<200] <- 4 group[id>=200 & id<250] <- 5 group[id>=250

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