similar to: Time-dependent coefficients in a Cox model with categorical variants

First, as others have said please obey the mailing list rules and turn of First, as others have said please obey the mailing list rules and turn off html, not everyone uses an html email client. Here is your code, formatted and with line numbers added. I also fixed one error: "y" should be "status". 1. fit0 <- coxph(Surv(futime, status) ~ x1 + x2 + x3, data = data0) 2. p

Hi, I am just wondering if there is a test available for testing if a linear fit of an independent variable in a Cox regression is enough? Thanks for any suggestions. John Zhang ____________________________________________________________________________________ [[elided Yahoo spam]]

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],

It seems to me that R returns the unpenalized log-likelihood for the ratio likelihood test when ridge regression Cox proportional model is implemented. Is this as expected? In the example below, if I am not mistaken, fit$loglik[2] is unpenalized log-likelihood for the final estimates of coefficients. I would expect to get the penalized log-likelihood. I would like to check if this is as expected.

I have a suggested addition to termplot. We have a local mod that is used whenever none of the termplot options is quite right. It is used here almost daily for Cox models in order to put the y axis on a risk scale: ---- fit <- coxph(Surv(time, status) ~ ph.ecog + pspline(age), data=lung) zz <- termplot(fit, se=TRUE, plot=FALSE) yy <- zz$age$y + outer(zz$age$se, c(0, -2, 2),

Dear all, I have a couple of questions regarding the survival:::cch function. 1) I notice that Prentice and Self-Prentice functions are giving identical standard errors (not by chance but by programming design) while their estimates are different. My guess is they are both using the standard error form from Self and Prentice (1986). I see that standard errors for both methods are

On examining non-linearity of Cox coefficients with penalized splines - I have not been able to dig up a completely clear description of the test performed in R or S-plus. >From the Therneau and Grambsch book (2000 - page 126) I gather that the test reported for "linear" has as its null hypothesis that the spline coefficient is the same at the center of basis. Thus, in the example

Hi all, Is there an equivalent to predict(...,type="linear") of a Proportional hazard model for a Cox model instead? For example, the Figure 13.12 in MASS (p384) is produced by: (aids.ps <- survreg(Surv(survtime + 0.9, status) ~ state + T.categ + pspline(age, df=6), data = Aidsp)) zz <- predict(aids.ps, data.frame(state = factor(rep("NSW", 83), levels =

Hi, I'm sure this should be simple but I can't figure it out! I want to get the median survival calculated by the survfit function and use the value rather than just be able to print it. Something like this: library(survival) data(lung) lung.byPS = survfit(Surv (time, status) ~ ph.ecog, data=lung) # lung.byPS Call: survfit(formula = Surv(time, status) ~ ph.ecog, data = lung) 1

I have a simple question that irritatingly I haven't been able to figure out on my own. It seems that some functions from the "Pspline" package are successfully installed while others are not. The code with which I'm working is more complicated, but the following highlights my problem. If I run the following code > tt <- seq (0,1,length=20) > xt <- tt^3 > fit

To whom it may concern, I don't know whether this is really a bug with the Pspline package or only a problem with my installation. Things work fine in Linux but not in Mac OS X (Darwin). Both system run the latest public versions of R and Pspline. predict.smooth.Pspline produces only NaN instead of predicted values when norder>2: > library (Pspline) > tt <- seq

Dear all, I'm trying to use the Pspline add-on package to fit a quintic spline (norder =3), but I keep running into a Singularity error. > traj.spl <- smooth.Pspline(time, x, norder=3 ) Error in smooth.Pspline(time, x, norder = 3) : Singularity error in solving equations > Playing around with the other parameters produces an "unused arguments" error: > traj.spl

Hi, I'm using a Cox-Regression to estimate hazard rates on prepayments. I'm using the "pspline" function to face non-linearity, but I have no clue how to interpret the result. Unfortunately I did not find enough information on the "pspline" function wether in the survival package nor using google.. I got following output: * library(survival)* > > > >

Hello, is there a R package that provides a log rank trend test for survival data in >=3 treatment groups? Or are there any comparable trend tests for survival data in R? Thanks a lot Markus -- Dipl. Inf. Markus Kreuz Universitaet Leipzig Institut fuer medizinische Informatik, Statistik und Epidemiologie (IMISE) Haertelstr. 16-18 D-04107 Leipzig Tel. +49 341 97 16 276 Fax. +49 341 97 16

Hi, Is it normal to get intercept in the list of covariates in the output of survreg function with standard error, z, p.value etc? Does it mean that intercept was fitted with the covariates? Does Value column represent coefficients or some thing else? Regards, ------------------------------------------------- tmp = survreg(Surv(futime, fustat) ~ ecog.ps + rx, ovarian,

Dear R users: I am not entirely convinced that clogit gives me the correct result when I use pspline() and maybe you could help correct me here. When I add a constant to my covariate I expect only the intercept to change, but not the coefficients. This is true (in clogit) when I assume a linear in the logit model, but the same does not happen when I use pspline(). If I did something similar

Hi, i need to use pspline. In this pspline function coxph.wtest was used. When I try to make some change to this function by pulling out the pspline function, it turns out R gave me an error msg, saying coxph.wtest cannot be found. Even if i dont change anything in pspline and just rename it and run the function, it did not work out. Can any one help me with this? is there anyway to get the

Dear all: Could anyone please provide some R codes to plot the below survival data to compare two groups (0 vs 1) after 2 simulations (TRL)? need 95% prediction interval on the plot from these 2 trials. I would like to simulate 1000 trials later. Thanks a lot for your great help and consideration! yan TRL ID ECOG BASE PTR8 GROUP POP ST ind 1 1 1 1 2.2636717 0.255634126 1 1 99.4 F 3 1 2 1

I was looking at how the model.frame method for lm works and comparing it to my own for coxph. The big difference is that I try to retain xlevels and predvars information for a new model frame, and lm does not. I use a call to model.frame in predict.coxph, which is why I went that route, but never noted the difference till now (preparing for my course in Nashville). Could someone shed light

Dear all, May I ask is there any functions in R to evaluate the fitness of "coxph" and "survreg" in survival analysis, please? For example, the results from Cox regression and Parametric survival analysis are shown below. Which method is prefered and how to see that / how to compare the methods? 1. coxph(formula = y ~ pspline(x1, df = 2))