search for: basehaz

I have a very general question about what the centering option in basehaz does to factors. (basehaz computes the baseline cumulative hazard for a coxph object using the Breslow estimator). Lets say I'm interested in a survival model with two (dichotomous) factors and a continuous covariate. Variable Possible Values Factor1 0 or 1 Factor...

Hello, I have a couple of questions with regards to fitting a coxph model to a data set in R: I have a very large dataset and wanted to get the baseline hazard using the basehaz() function in the package : 'survival'. If I use all the covariates then the output from basehaz(fit), where fit is a model fit using coxph(), gives 507 unique values for the time and the corresponding cumulative hazard function. However if I use a subset of the varaibles, basehaz() gives 6...

I am unable to get the basehaz function to apply a proportional hazards model to a new data frame. I replicated my specific situation with the example for coxph in the help, where I changed the x value of the first record from 0 to 1. Is there something incorrect in the syntax that I am using? Thanks in advance! test1 <-...

...de, 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 very large dataset and wanted to get the baseline hazard using the > basehaz() function in the package : 'survival'. > If I use all the covariates then the output from basehaz(fit), where fit is > a model fit using coxph(), gives 507 unique values for the time and the > corresponding cumulative hazard function. However if I use a subset of the > varaible...

Hello R-users. I believe that the way basehaz (in the survival package) compute the baseline hazard function is false. I come to question this function when it gives me hazard probabilities greater than 1. Looking at the code I think I've localised the error : hazard probability is computed as : H <- -log(surv) but it seems to me...

...h the package survival (v. 2.34-1) and a log-log plot obtained using Design (v. 2.1-2). For two variables, the lines in the survival curves crossed. The statistician I been obtaining advice from (who does not use R) asked me to obtain the hazard function values. I am wanting to confirm whether basehaz is the correct command to obtain such values, to better understand what is occurring in the log plots. Below is the code that I have been using. Any assistance is appreciated, regards Bob library(survival) recidivismv <- read.csv("g://Chapsurv_v.csv",header=T) cox.V <- coxph...

I want use survfit() and basehaz() inside a function, but it doesn't work. Could you take a look at this problem. Thanks for your help. Following is my codes: library(survival) n <- 50 # total sample size nclust <- 5 # number of clusters clusters <- rep(1:nclust,each=n/nclust) beta0 <- c(1,2) set.seed(13) #...

Dear R-help, I am trying to obtain the baseline survival estimate of a fitted Cox model (S_0 (t)). I know that previous posts have said use 'basehaz' but this gives the baseline hazard function and not the baseline survival estimate. Is there a way to obtain the baseline survival estimate or do I have to use the formula which does something like S(t) = exp[- the integral from 0 to t of h(u) du]? Thank you for your assistance, Laura fit1...

...I perform a stratified cox and then I need the strata as a  numeric array "straft.ln" ft.ln <- coxph(Surv(times,deaths)~ages+chemos+chemos:f1+chemos:f2+horms+horms:f1+horms:f2+grades+grades:f1+grades:f2+positives+positives:f1+positives:f2+sizes+sizes:f1+sizes:f2+strata(stra),data=ddd) basehazzft.ln=basehaz(ft.ln,centered=FALSE) H0ft.ln=c(basehazzft.ln[,1]) timeft.ln=c(basehazzft.ln[,2]) straft.ln=c(basehazzft.ln[,3]) I notice that basehazzft.ln[,3] is not the same with straft.ln with respect to the values i.e.: (basehazzft.ln$stra[1]) [1] stra=1 134 Levels: stra=1 stra=10 stra=100 str...

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

...)^exp(X_iB) is the expression for the survival function And -ln(S_o(t_i) ) is the expression for the cumulative baseline hazard function, H_o(t_i) Then by rearranging the expression for the survival function I get the following: -ln(S_o(t_i) ) = -ln( S(t_i|X_i,B) ) / exp(X_iB) = basehaz(fit)/exp(fit$linear.predictors) Am I right so far and is there an easier way? The plot of the cumulative baseline hazard function , H_o(t_i), should be linear across time. Once I have, H_o(t_i), to get at h_o(t_i) I then need to reverse the cumsum operation. The corresponding plot should have a c...

...cumulative mortality hazard over time for both males and females with associated confidence intervals. I would like to make sure I obtained proper estimates of the cumulative hazard for both males and females with associated confidence intervals but I have doubts regarding my calculations # I used basehaz ?H0? to get to the baseline hazard (covariate level 0, males in this case) basehazard <- basehaz(M1, centered=FALSE)[,1] NBLOWCI <- basehazard * exp(1.96*sqrt(M1$var[1])) NBHIGHCI <- basehazard * exp(-1.96*sqrt(M1$var[1])) # I applied the following equation H0*exp(Beta*X) to get to the...

...Can anyone explain to me the reason for the discrepancy? require(foreign) gb <- read.dta("GB.dta") # Green & Byar data; N = 483 # Method 1 fit1 <- coxph( Surv(time, status=="Cancer" | status=="CVD" | status=="Other") ~ 1, data=gb) h1 <- basehaz(fit1) # Method 2 fit2 <- survfit(Surv(time, status=="Cancer" | status=="CVD" | status=="Other") ~ 1, data=gb) jump <- fit2$n.event > 0 h2 <- cumsum(fit2$n.event[jump]/fit2$n.risk[jump]) plot(h1$time, h1$hazard - h2) Thank you, Ravi. _________________...

...zard (e.g. by package pspline) and ask for its derivative" (https://stat.ethz.ch/pipermail/r-help/2004-July/052376.html) the following code could be use to estimate (and plot) a smooth baseline hazard: > t<-seq(min(data$start),max(data$stop),length=100) > lines(t, predict(sm.spline(x=basehaz(coxfit)[,2],y=basehaz(coxfit)[,1],norder=2), t,1)) #there is a problem with this code. One should add the contraint that the baseline hazard cannot be negative. The following computes the parametric part of the cox model. > risk <- predict(coxfit, type='risk') # gives exp(X'b) s...

Dear list, is there a function available which provides an estimate of the hazard function based on a cox proportional hazard model? I only found the cumulative hazard and the survival function as survfit options. Thanks for your help Peter

...ant to predict the cumulative hazard for each person for their next event at a given time in future.What is the best way to compute it? Currently I am doing it by pulling the different elements and putting them together using my own code. For example, I get the cumulative baseline hazard from the basehaz function bh<-basehaz(m) bh$hazard for the strata in which the predicted event is and the time that matches to the prediction time. xbeta from predict function xbeta<-predict(m,newdata,type="lp") fval<-m$frail Regards Zainab

...60313783 2.39101856 1.20878208 0.18226221 0.39845758 [7] 0.33727506 0.23804627 0.02622108 What do the means fitf2$means[6:9] correspond to? I am asking these questions because I would like to clarify how the frailty terms enter into the computation of the baseline cumulative hazard via the basehaz(fitf, centered=FALSE) function. It would seem that the way the basehaz function operates is different dependening upon how many frailty groups there are. Thank you very much for your help. giacomo

Hi everyone. There's been several threads on baseline hazard in Cox model but I think they were all on cumulative baseline hazard, for instance http://tolstoy.newcastle.edu.au/R/help/01a/0464.html http://tolstoy.newcastle.edu.au/R/help/01a/0436.html "basehaz" in package survival seems to do a cumulative hazard. extract from the basehaz function: sfit <- survfit(fit) H <- -log(sfit$surv) Since sfit$surv is monotonic, H will be monotonic too, which makes me think it is a cumulative function. I think H(t) it is the "sum" (&q...

...d function(H_0(t)) ## Hazard.Function <- -log(MODEL.COXPH.SURVFIT.SURV) ################################################ ################################################ # Baseline hazard function can also be calculated explicitly ## MODEL.COXPH.BASELINEHAZ <- basehaz(MODEL.COXPH) ########################################################################################## ########################################################################################## ########################################################################################## ############...

Hi, If I fit a Cox model using "coxph", is there a R function so that I could obtain the estimate of baseline survival function? Thank you. Zheng -- Zheng Yuan Ph.D student Department of Biostatistics University of Michigan Ann Arbor, MI 48109