similar to: survreg (survival) reports erroneous results for left-censored (PR#2291)

Thank you for looking into this so quickly. As you correctly surmise, I was using the Carbon version of R-1.6.1 on Mac OS 10.2.2 (Jaguar) when I got the "wrong" answers. One other observation: The right censoring seems to work fine. Thanks again, Tim On Thursday, November 14, 2002, at 11:09 AM, Jan de Leeuw wrote: > I take that back. I now get the "correct" result

Full_Name: Tim Cohn Version: 1.6.1 OS: Macintosh OS X Submission from: (NULL) (130.11.34.250) The Mac version of survreg does not handle left-censored data correctly (at least the results are not what I get doing it other ways, and they are not the same as I get running R 1.6.1 in Windows 98se; the Windows 98 results are correct). On the windows version of R 1.6.1. >

Hello, I'm working with some left censored survival data using accelerated failure time models. I am interested in fitting different distributions to the data but seem to be getting the same results from the model fit using survreg regardless of the assumed distribution. These two codes seem to provide the same results: aft.gaussian <-

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

Can anybody give me a neat example of interval censored data analysis codes in R? Given that suvreg(Surv(c(1,1,NA,3),c(2,NA,2,3),type="interval2")~1) works why does survreg(Surv(data[,1],data[,2],type="interval2")~1) not work where data is : T.1 T.2 Status 1 0.0000000 0.62873036 1 2 0.0000000 2.07039068 1 3 0.0000000

I am trying to fit a lognormal distribution on interval-censored data. Some of my intervals have a lower bound of zero. Unfortunately, it seems like survreg() cannot deal with lower bounds of zero, despite the fact that plnorm(0)==0 and pnorm(-Inf)==0 are well defined. Below is a short example to reproduce the problem. Does anyone know why survreg() must behave that way? Is there an alternate

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-users, I know that a few people have asked whether survreg handles left-truncation data and the reply that i have seen so far is that it does. However, when I try to use survreg on left-truncated data, I got the following error message. > survcs3<-survreg(Surv(start,end,status)~AG, data=DPONEcs3, dist="exponential") Error in survreg(Surv(start, end, status) ~ AG, data =

Dear All, I would like to fit some parametric survival models using left truncated, right censored data in R. However I am having problems finding a function to fit parametric survival models which can handle left truncated data. I have tested both the survreg function in package survival: fit1 <- survreg(Surv(start, stop, status) ~ X + Y + Z, data=data1) and the psm function in package

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Hello, I have interval censored data, censored between (0, 100). I used the tobit function in the AER package which in turn backs on survreg. Actually I'm struggling with the distribution. Data is asymmetrically distributed, so first choice would be a Weibull distribution. Unfortunately the Weibull doesn't allow for zero values in time data, as it requires x > 0. So I tried the

dear all, I would like to know whether survdiff and survReg function in the survival package work for left-truncated and right-censored data. If not, what other functions can i use to make comparison between two survival curves with LTRC data. thanks for any help given sing yee

Dear R Listers- I am attempting to analyse the survival of seeds in cages (exclosures) that differ in their permeability to rainforest mammals. Because I did not observe the moment of seed disappearance, my data is interval censored. This limits my options for analysis (as I understand it) to survreg, in the survival package. Because I repeated the experiment in 8 sites, I have a random

Hello all, I want to extract the survival function estimate from a model fitted by survreg(). Using predict.survreg(..., type="quantile", p=seq(0,1,0.001)), gives the quantiles, which I managed to turn around into a survival function estimate (Prob{T > t} as function of t). Is there a more straightforward way of doing this? I have had difficulties using pweibull() with the

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),

Hi all. Belove is the example from the cluster-help page wtih the output. I simply cannot figure out how to relate the estimate and robust Std. Err to the p-value. I am aware this a marginal model applying the sandwich estimator using (here I guess) an emperical (unstructered/exchangeable?) ICC. Shouldent it be, at least to some extend, comparable to the robust z-test, for rx :

dear r experts: It seems my message got spam filtered, another try: i would appreciate advice on how to get the p-value from the object 'sr' created with the function survreg() as given below. vlad sr<-survreg(s~groups, dist="gaussian") Coefficients: (Intercept) groups -0.02138485 0.03868351 Scale= 0.01789372 Loglik(model)= 31.1 Loglik(intercept only)= 25.4

dear r experts: I would appreciate advice on how to get the p-value from the object 'sr' created with the function survreg() as given below. vlad sr<-survreg(s~groups, dist="gaussian") Coefficients: (Intercept) groups -0.02138485 0.03868351 Scale= 0.01789372 Loglik(model)= 31.1 Loglik(intercept only)= 25.4 Chisq= 11.39 on 1 degrees of freedom, p= 0.00074 n=

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 have some datasets, all left-censoring, and I would like to fit distributions to (weibull,exponential, etc..). I read one solution using the function survreg in the survival package. i.e survreg(Surv(...)~1, dist="weibull") but it returns only the scale parameter. Does anyone know how to successfully fit the exponential, weibull etc... distributions to left-censoring