search for: moeschberger

...lized estimating equations solver for parameters in mean, scale, and correlation structures, through mean link, scale link, and correlation link. Can also handle clustered categorical responses. License: GPL version 2 or later. The second package KMsurv is a data package for the textbook Klein and Moeschberger (1997), Survival Analysis. It may be useful in teaching or studying such a course. The DESCRIPTION file is Package: KMsurv Version: 0.1-1 Date: 2002/05/18 Title: Data sets from Klein and Moeschberger (1997), Survival Analysis Author: Original by Klein and Moeschberger modifications by Jun Yan...

Dear all, I am looking for a good reference on "Survival analysis". I am looking for a booking containing both applications and Maths. Explaining different methods in survival analysis .... Many thanks Bernard --------------------------------- [[alternative HTML version deleted]]

...ic, lognormal and log-logistic, which correspond to certain distributions for the error terms. I'm wondering if there is a package or how to obtain the parameter estimates (the beta's are of great interest) from the AFT model (maximizing log-likelihood with censored data as in Klein and Moeschberger book) if we have 1) error ~ Cauchy(0,1) 2) error comes from a contaminated gaussian dist., for example, density of error, f(x) = 0.9*phi(x, mean=0, sd=1) + 0.1*phi(x, mean=0, sd=3), where phi(.) is the standard gaussian pdf....

Has anybody written an actuarial (life) survival procedure, this does not appear to be an option in the survival package? This approach is common in orthopaedic surgery to demonstrate the survival of prostheses. I need to apply the "modified" lower conf.int because of the censoring over time. I want a life table which I can then easily plot. Many thanks

...ople <- 1000 al <- 0.1 bet <- 0.1 lifetimes <- random.gomp(n=no.people, alpha=al, beta=bet) ### Since I neither have censoring nor truncation in this simple case, ### the log-likelihood should be simply the sum of the log of the ### the densities (following the parametrization of Klein/Moeschberger ### Survival Analysis, p. 38) loggomp <- function(alphas, betas, timep) { return(sum(log(alphas) + betas*timep + (alphas/betas * (1-exp(betas*timep))))) } ### Now I thought I could obtain a matrix of the log-likelihood surface ### by specifying possible values for alpha and beta with the giv...

...ople <- 1000 al <- 0.1 bet <- 0.1 lifetimes <- random.gomp(n=no.people, alpha=al, beta=bet) ### Since I neither have censoring nor truncation in this simple case, ### the log-likelihood should be simply the sum of the log of the ### the densities (following the parametrization of Klein/Moeschberger ### Survival Analysis, p. 38) loggomp <- function(alphas, betas, timep) { return(sum(log(alphas) + betas*timep + (alphas/betas * (1-exp(betas*timep))))) } ### Now I thought I could obtain a matrix of the log-likelihood surface ### by specifying possible values for alpha and beta with the giv...

Hello R-users. I believe that the way basehaz (in the survival package) compute the baseline hazard function is false. I come to question this function when it gives me hazard probabilities greater than 1. Looking at the code I think I've localised the error : hazard probability is computed as : H <- -log(surv) but it seems to me that hazard probabilities is rather an instantaneous

in R when carring out the log rank test is the censored variable denoted by 1 or 0 or its of no consequence. thanks --------------------------------- always stay connected to friends. [[alternative HTML version deleted]]

Does anybody know if there is such a function to estimate the distribution for interval censored data? survfit doesn't work for this type of data as I tried various references. [[alternative HTML version deleted]]

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]]

Hello, I have a question about the function lifetab in package KMsurv. The description of the output value surv says "the estimated survival function at the start of the intervals". Are these estimates the ones calculated via Kaplan-Meier probability of survival ? Thanks in advance! -- View this message in context:

Is there an implementation of the Cox-Snell residuals / Nelson-Aalen plot for goodness of fit? Or otherwise is there an appropriate Goodness of Fit diagnostic? Thanks Murray -- Murray Pung Statistician, Datapharm Australia Pty Ltd 0404 273 283 [[alternative HTML version deleted]]

Dear List, Does anybody no how to compare mean survival times for two (more) groups in R? What test statistics should I use? Thank you very much! Joe [[alternative HTML version deleted]]

Well, I would suggest using the code already in place in the survival package. Here is my code for your problem. I'm using a copy of the larynx data as found from the web resources for the Klein and Moeschberger book. larynx <- read.table("larynx.dat", skip=12, col.names=c("stage", "time", "age", "year", "death")) larynx$stage <- factor(larynx$stage) larynx$age.grp <- cut(larynx$age, c(0, 55, 70, 90),...

...case, > >>>>> > >>>>> > >>>>>> ### the log-likelihood should be simply the sum of the > log of the > >>>>>> ### the densities (following the parametrization of > >>>>> > >>>>> Klein/Moeschberger > >>>>> > >>>>> > >>>>>> ### Survival Analysis, p. 38) > >>>>>> > >>>>>> loggomp <- function(alphas, betas, timep) { > >>>>>> return(sum(log(alphas) + betas*timep + (alphas/bet...