similar to: Time-dependent covarites in survreg function

Dear all, I'm asking this question again as I didn't get a reply last time: I'm doing a survival analysis with time-dependent covariates. Until now, I have used a simple Cox model for this, specifically the coxph function from the survival library. Now, I would like to try out an accelerated failure time model with a parametric specification as implemented for example in the survreg

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

Thanks for sharing the questions and responses! Is it possible to appreciate how much the coefficients matter in one or the other model? Say, using Biau's example, using coxph, as.factor(grade2 == "high")TRUE gives hazard ratio 1.27 (rounded). As clinician I can grasp this HR as 27% relative increase. I can relate with other published results. With survreg the Weibull model gives a

Hi, I would like to fit parametric survival models to time-to-event data that are left truncated. I have checked the help page for survreg and looked in the R-help archive, and it appears that the R function survreg from the survival library (version 2.16) should allow me to take account of left truncation. However, when I try the command

Hi everybody. I'm trying to fit a weibull survival model with a spline basis for the predictor, using the survival library. I've noticed that it doesn't seem to be possible to use the aic method to choose the degrees of freedom for the spline basis in a parametric regression (although it's fine with the cox model, or if the degrees of freedom are specified directly by the user),

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 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

Why do I get different sign of the coefficients of covariates when I run the semi-parametric proportional hazard model (coxph) compared to the parametric proportional hazard model (survreg)? Anyone with experience in extracting information form survreg to make predictions are free to contact me. Cheers, Ullrika [[alternative HTML version deleted]]

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

Dear R-users, I need to use the aftreg function in package 'eha' to estimate failure times for left truncated survival data. Apparently, survreg still cannot fit such models. Both functions should be fitting the accelerated failure time (Weibull) model. However, as G?ran Brostr?m points out in the help file for aftreg, the parameterisation is different giving rise to different

I am using psm to model some parametric survival data, the data is for length of stay in an emergency department. There are several ways a patient's stay in the emergency department can end (discharge, admit, etc..) so I am looking at modeling the effects of several covariates on the various outcomes. Initially I am trying to fit a survival model for each type of outcome using the psm

---------------------------- Original Message ---------------------------- Subject: Different results from survreg with version 2.6.1 and 2.10.1 From: nathalcs at ulrik.uio.no Date: Fri, March 19, 2010 16:00 To: r-help at r-project.org -------------------------------------------------------------------------- Dear all I'm using survreg command in package survival.

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))

Dear All, I am doing a parametric survival analysis with: fit <- survreg(Surv(xyz$start, xyz$stop, xyz$event, type="interval") ~ 1, dist='loglogistic') At this point I do not want to look into covariates, hence the '~1' as model formulation. As event types I have exact, interval, and right censored lifetime data. Everything works fine. For reasons that are

Dear all, I am trying to find a suitable R-function for parametric proportional hazard regressions. The package survival contains the coxph() function which performs a Cox regression which leaves the base hazard unspecified, i.e. it is a semi-parametric method. The package Design contains the function pphsm() which is good for parametric proportional hazard regressions when the underlying base

I am not sure if this is the right place for that kind of questions, but I wondered that the recommended package survival did not pass R's check procedure without warnings: 1) unbalanced braces: * Rd files with unbalanced braces: * man/Surv.Rd * man/cluster.Rd * man/cox.zph.Rd * man/coxph.Rd * man/coxph.detail.Rd * man/date.ddmmmyy.Rd * man/lines.survfit.Rd *

Hi all, I am looking at the tutorial/appendix from John Fox on ?Cox Proportional-Hazards Regression for Survival Data? available here: http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-cox-regression.pdf I am particularly interested in modelling survival with time-dependent covariates (Section 4). The data look like this: > Rossi.2[1:50,] start stop arrest.time week arrest fin

Dear r-help subscribers, I am working on some survival analysis of some interval censored failure time data in R. I have done similar analysis before using PROC LIFEREG in SAS. In that instance, a gamma survival function was the optimum parametric model for describing the survival and hazard functions. I would like to be able to use a gamma function in R, but apparently the survival package does

Can survreg() handle interval-censored data like the documentation says? I ask because the command: survreg(Surv(start, stop, event) ~ 1, data = heart) fails with the error message Invalid survival type yet the documentation for Surv() states: "Presently, the only methods allowing interval censored data are the parametric models computed by 'survreg'"

Dear r-help subscribers, A couple of weeks ago I sent the following message to the r-help mail list. It hasn't generated any response, and I could really use some help on this. Anyone able to help? Thanks again, Roger Dungan >> I am working on some survival analysis of some interval censored failure time data in R. I have done similar analysis before using PROC LIFEREG in SAS. In