similar to: How to get predicted time from coxph model

Hello All, Does anyone know why length(fit1$time) < length(fit2$n) in survfit.coxph output? Why is the predicted time length is not the same as the number of samples (n)? I tried: example(survfit.coxph). Thanks, parmee > fit2$n [1] 241 > fit2$time [1] 0 31 32 60 61 152 153 174 273 277 362 365 499 517 518 547 [17] 566 638 700 760 791

Hi all, I have some questions about the predicted HR in coxph function including psline covariate. If just fitting covariate as linear form, by default, the reference value for each of predictions type (linear predictor, risk and terms) is the mean covariate within strata. If the psline is specified for the covariate, what's the reference value for the predictions? I did some test code

I would like to extract predicted failure times from a coxph model in library(survival). However, none of the prediction options ("lp", "risk", "expected", "terms") seem to bear any relationship to failure time. Perhaps I am asking the wrong question, but can coxph provide predicted failure times? Thanks, Dan Bebber Department of Plant Sciences University

Dear all, I have a question about time-dependent covariates in a coxph model. Specifically I am wondering whether it is possible to give more recent events a higher weight when constructing time-dependent covariates. Assume I have a sample of cancer patients and I would like to predict whether the number of treatments a patient received has an impact on survival time. For each patient in my

Dear List, Because Cox proportional hazards model didn't give the baseline hazard function, how to calculate the predictive probability for each test sample at a special time point,such as 5-year or 10-year ? In survival package, predict.coxph() function gives three different type of predicted value, for the type of "expected", Does it mean the expected number of events for a

I fit a coxph model: coxphfit <- coxph(Surv(sampledLifetime, !sampledCensoredQ) ~ curpbc6 + prevpbc6, sampledTimeSeries) Now I'm trying to predict the expected number of events using a new dataset. The documentation suggests that coxPred <- predict(coxphfit, newdata = testTimeSeries, type="expected") will do what I want, but I get the error Error in

Given a cox model: library(Hmisc); library(survival); (library(Design); cox.model=cph(Surv(futime, fustat) ~ age, data=ovarian, surv=T) str(cox.model) What I need is the total estimated time until failure (death), not the probability of failing at a given time (survival probability), or hazard etc, which is what I get from survest and predict for example. I suspect the answer is

Dear all, I have been trying to plot hazard function in R for survival data, but in vain. Can anybody help me out in plotting hazard function in R? Dr Suman Kumar -- View this message in context: http://r.789695.n4.nabble.com/how-to-plot-hazard-function-for-coxph-model-tp4637953.html Sent from the R help mailing list archive at Nabble.com.

Dear all, I have a data set<http://yaap.it/paste/c11b9fdcfd68d02b#gIVtLrrme3MaiQd9hHy1zcTjRq7VsVQ8eAZ2fol1lUc=>with 6 clusters, each containing 48 (possibly censored, in which case "event = 0") survival times. The "x" column contains a binary explanatory variable. I try to describe that data with a gamma frailty model as follows: library(survival) mod <-

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

Hi there, I have a predictor varible "class" which is a categorical variable and a ' coxph' is used to find the coeffients. How can I plot the predicted survival proportion based on this model? Thanks Lisa Wang Princess Margaret Hospital Toronto tel 416 946 4501

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 would like to get the likelihood ratio and score tests for specific variables in a multivariate coxph model. The default is Wald, so the tests for each separate variable is based on Wald's test. I have the other tests for the full model but I don't know how to get them for each variable. Any idea? David Biau. [[alternative HTML version deleted]]

I am fitting a cox PH model w/ 2 predictors, x1 = 0/1 treatment variable and x2=continuous variable. I am using natural splines (ns) to model the effect of x2. I would like to examine the estimated effect of x2 on the hazard. I have tried various approaches (below; let model.fit= fitted model using coxph in survival library): 1. The simplest method appears to be using termplot(model.fit).

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

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

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, I think the coxph and survfit.coxph can give the cumulative hazard of cox model. But is there any method to calculate the hazard Lambda(t)=lambda_0(t)*exp{beta*X(t)}? Any suggestion will be great help. Thank you very much! Koshihaku -- View this message in context: http://r.789695.n4.nabble.com/How-to-get-the-hazard-of-coxph-not-cumulative-hazard-tp3873516p3873516.html Sent from the R

Hi, I am attempting to evaluate the prediction error of a coxph model that was built after feature selection with glmnet. In the preprocessing stage I used na.omit (dataset) to remove NAs. I reconstructed all my factor variables into binary variables with dummies (using model.matrix) I then used glmnet lasso to fit a cox model and select the best performing features. Then I fit a coxph model

Now that we have case cohort model , we have 1000 people and 50 cases Let the first 10 cases occur at the same time second 10 " third 10 " fourth 10 " fifth 10 " How easy is it to randomly sample 50 different cohort controls for each group? That is: randomly sample 50 cohort