similar to: Survival problem with two populations?

Hi I have been asked to provide Weibull parameters from a paper using Kaplan Meir survival analysis. This is something I am not familiar with. The survival analysis in R works nicely and is the same as commercial software (only the graphs are superior in R). The Weibull does not and produces an error (see below). Any ideas why this error should occur? My approach may be spurious.

OK this is bound to be something silly as I'm completely new to R - having started using it yesterday. However I am already warming to its lack of 'proper' GUI... I like being able to rerun a command by editing one parameter easily... try and do that in a Excel Chart Wizzard! I eventually want to use it to analyse some chemotherapy response / survival data. That data will not be

Dear help news reader, I'm trying to draw a Kaplan-Meier curve and would like to ask the news group for some help Supposing I have study comapring two drugs, "A", and "B" and I recorde the time to get to the clinical endpoint (Time), in my case becommming virus free. I have setup the following frame: Time c Drug 1 5 1 A 2 7 1 B 3 2 1 A 4 10 1

Dear R-Helpers, Apologies in advance as this is partly (widely ?) OT. Not sure where to ask, R is my favorite computer tool (no kidding) and there are plenty of knowledgable and helpful people on the list. Background: There are discussions among experts and regulatory authorities (cf guideline http://www.emea.europa.eu/pdfs/human/ewp/056598en.pdf) that, in for example Amyotrophic Lateral

Dear all, I'm trying to fit a curve to some 1 year failure-time data, so that I can extrapolate and predict failure rates up to 3 years. The data is in the general form: Treatment Time Status Treatment A 28 0 Treatment B 28 0 Treatment B 28 0 Treatment A 28

Calum had a long question about drawing survival curves after fitting a Weibull model, using pweibull, which I have not reproduced. It is easier to get survival curves using the predict function. Here is a simple example: > library(survival) > tfit <- survreg(Surv(time, status) ~ factor(ph.ecog), data=lung) > table(lung$ph.ecog) 0 1 2 3 <NA> 63 113 50 1

Hi to all, I am working on design package using survival function. First using PSM and adopting a weibull specification for the baseline hazard , I have got the following results(since weibull has both PH and AFT propreties ,in addition I have used the PPHSm command): Value Std. Error z p (Intercept) 1.768 1.0007 1.77 7.73e-02 SIZE -0.707 0.0895 -7.90 2.80e-15

Dear all, I have data from 1970 to 1990 for people above age 50. Now I want to calculate survival curves by age starting at age 50 using the Kaplan Meier Estimator. The problem I have is that there are already people in 1970 who are older than 50 years. I guess this is called delayed entry or left truncation (?). I thought the code would be: roland <- survfit(Surv(time=age.enter,

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, Please help on this. We will be teaching epidemiology using opensource software. What are R built-in functions or functions in available packages that are capable of doing these: a) Logistic regression (glm?) b) Conditional logistic regression c) Logistic regression with random effects d) Beta-binomial regression e) Poisson regression f) Weibull regression (eha?) g) Exponential

Dear R-Helpers, I am hoping to perform survival analyses using the "ex-Gaussian" distribution. I understand that the ex-Gaussian is a convolution of exponential and Gaussian distributions for survival data. I checked the "survreg.distributions" help and saw that it is possible to mix pre-defined distributions. Am I correct to think that the following code makes the

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,

Hi I am using R to fit a survival function to my data (with a weibull distribution). Data: Survival of individuals in relation to 4 treatments ('a','b','c','g') syntax: ---- > survreg(Surv(date2)~males2, dist='weibull') But I have some problems interpreting the outcome and getting the parameters for each curve. --------- Value Std.

Dear R users, i try to calculate the probabilty to survive a given time by using the estimated survival curve by kaplan meier. What is the right way to do that? as far as is see i cannot use the predict-methods from the survival package? library(survival) set.seed(1) time <- cumsum(rexp(1000)/10) status <- rbinom(1000, 1, 0.5) ## kaplan meier estimates fit <- survfit(Surv(time,

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

Dear all, I have a stat question that may not be related to R, but I would like to have your advice. I have just read a medical paper in which the authors report the 1-p (where p is the cumulative survival probability from the Kaplan Meier curve) as incidence of disease. Specifically, the study followed ~12000 women on drug A and ~20000 women on drug B for 12 months. During that period

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,

Hi, I need a help in a survival analysis using survreg function with weibull distribution from survival package. Look the data sample: ########## Start of script dados <- structure(list(TFD = c(20L, 34L, 1L, 2L, 3L, 3L, 50L, 26L, 1L, 50L, 21L, 3L, 13L, 11L, 22L, 50L, 50L, 1L, 50L, 9L, 50L, 1L, 13L, 50L, 50L, 1L, 6L, 50L, 50L, 50L, 36L, 3L, 46L, 10L, 50L, 1L, 18L, 3L, 36L, 37L, 50L, 7L, 1L,

Dear R-users, I'd like to announce the release of the new package JM (JM_0.1-0 available from CRAN) for the joint modelling of longitudinal and time-to-event data. The package has a single model-fitting function called jointModel(), which accepts as main arguments a linear mixed effects object fit returned by function lme() of package nlme, and a survival object fit returned by either

Dear R-users, I'd like to announce the release of the new package JM (JM_0.1-0 available from CRAN) for the joint modelling of longitudinal and time-to-event data. The package has a single model-fitting function called jointModel(), which accepts as main arguments a linear mixed effects object fit returned by function lme() of package nlme, and a survival object fit returned by either