search for: weibreg

Hi, I was wondering if someone can help me interpret the results of running weibreg. I run the following and get the following R output. > weibreg(Surv(time, censor)~covar) fit$fail = 0 Call: weibreg(formula = Surv(time, censor)~covar) Covariate Mean Coef Rel.Risk L-R p Wald p covar 319.880 -0.002 0.998 0.000 log(scale) 0.000...

...ible= 0.961 ) Likelihood ratio test= 91.2 on 2 df, p=0 Wald test = 85.3 on 2 df, p=0 Score (logrank) test = 106 on 2 df, p=0 Am I doing something wrong or is there something wrong with survreg? By the way, I have found a way to fit a Weibull model to left truncated data using weibreg from G?ran Brostr?m's library(eha): >library(eha) >summary(weibreg(Surv(t,y,d,type="counting")~x2+x3,data=statadata0,na.action=na.exclude)) fit$fail = 0 Call: weibreg(formula = Surv(t, y, d, type = "counting") ~ x2 + x3, data = statadata0, na.action = na.exc...

Dear R experts, How should lambda and gamma (with std.errors) be calculated for a weibull model with age as an independent predictor? I have assumed that this can be done with survreg with e. g. (summary(survreg(Surv(time, status) ~ age, dist = 'weibull')) ) and predict.survreg with e.g. (predict(model, se.fit = T, newdata = data.frame(age = seq(50, 80, 5)) but unfortunately I'm

...a=lung, na.action=na.omit) survplot(lung.wbd) Returns the error msg: Error in survplot.Design(lung.wbd) : fit does not have design information Using the eha package (I have not figured out how to get baseline function only, but have used ht=0/1 hypertension as a covariate): lung.wbe <- weibreg (Surv (futime, status)~ ht, y=T, data=audit, na.action=na.omit) plot (lung.wbe) I get a plot with hazard (y) against age (x) ??? I cannot control the axes with labeling and any other covariate gets the same plot. I have tried using covariates in the Design and Survival packages, but they alwa...

...sm function in package Design: fit2 <- psm(Surv(start, stop, status) ~ X + Y + Z, data=data1) But neither function appears to work with left truncated data. The error message "Invalid survival type" is received for both functions when left truncated data is specified. The function weibreg in the eha package fits Weibull survival models and works with left truncated data. This function is useful, but I need to fit parametric models other than Weibull. Any suggestions of functions that can fit parametric survival models other than Weibull on left truncated, right censored data would...

...y untied data this results in ordinary Cox regression. "MPPL" can be regarded as an attempt to handle tied data in Cox regression, comparable to the 'efron' method. This method does not break down because of too heavily tied data, which the efron method might do. 3. 'weibreg': Weibull regression for left truncated and right censored data. Allows for stratification with different shape and scale parameters in the strata. Moreover, there are functions for extracting subsamples as 'rectangles' in the Lexis diagram, including external ('communal') c...

A new version (0.96.3) of the package 'eha' is now on CRAN. Apart from some bug fixes, there are some news. The most noteworthy are: (i) 'plot.Surv' has a few new options, (ii) 'weibreg' now can fit null models (i.e., only scale and shape) and covariates are no longer automatically centered (although this is still the default), (iii) there is a new function, 'toBinary', which transforms a data frame suitable for survival analysis to a data frame suitable for ,eg, l...

...y untied data this results in ordinary Cox regression. "MPPL" can be regarded as an attempt to handle tied data in Cox regression, comparable to the 'efron' method. This method does not break down because of too heavily tied data, which the efron method might do. 3. 'weibreg': Weibull regression for left truncated and right censored data. Allows for stratification with different shape and scale parameters in the strata. Moreover, there are functions for extracting subsamples as 'rectangles' in the Lexis diagram, including external ('communal') c...

Hello I would like to compare the results obtained with a classical non parametric proportionnal hazard model with a parametric proportionnal hazard model using a Weibull. How can we obtain the equivalence of the parameters using coxph(non parametric model) and survreg(parametric model) ? Thanks Virginie

I was wondering if someone familiar with survival analysis can help me with the following. I would like to fit a Weibull curve, that may be dependent on a covariate, my dataframe "labdata" that has the fields "cov", "time", and "censor". Do I do the following? wieb<-survreg(Surv(labdata$time, labadata$censor)~labdata$cov,

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