similar to: Discrepancy in the PBC data set

Hi, When I run the following Cox proportional hazards model on the Mayo clinic's PBC data set (given in the "survival" package), the regression coefficients do not agree with the results presented in Table 4.6.3 (p. 195) of Fleming & Harrington's book. library(survival) data(pbc) ans.cox <- coxph(Surv(time, status) ~ log(bili) + log(alb) + age + log(protime) +

Hi there, Can anyone please help me because I am going to get crazy with the pbc data set. I just want to apply simple cox regression in the data set. I am a beginner in R but I don't think I am doing anything wrong. I have the book of Fleming and Harrington 1990. I perform cox regression by typing: out<- coxph(Surv(times/365,status)~log(bili)+log(proth)+edema+log(albumin)+age) out

Hi, I'm reading van Belle et al "Biostatistics" and trying to run a cox test using a dataset from: http://faculty.washington.edu/~heagerty/Books/Biostatistics/chapter16.html (Primary Biliary Cirrhosis data link at top of the page), I'm using the following code: --------------- start of code library(survival) liver <-

Hi: I have a few clarification questions about the elements returned by the coxph function used in conjuction with a frailty term. I create the following group variable: group <- NULL group[id<50] <- 1 group[id>=50 & id<100] <- 2 group[id>=100 & id<150] <- 3 group[id>=150 & id<200] <- 4 group[id>=200 & id<250] <- 5 group[id>=250

Hello, I probably have a syntax error in trying to generate an expected survival curve from a weighted cox model, but I can't see it. I used the help sample code to generate a weighted model, with the addition of a "weights=albumin" argument (I only chose albumin because it had no missing values, not because of any real relevance). Below are my code with the resulting error

Hello, I'm attempting to use the ratetable argument to survexp in the survival package. I use the example from the ?survexp help page below, and then slightly modify it to produce an error. library(survival) data(pbc) #fit a model without any factors pfit1 <- coxph(Surv(time, status > 0) ~ trt + log(bili) + log(protime) + age + platelet, data=pbc) #this works as expected

Dear All, I'm trying to teach myself latex along with the latex function in Hmisc and have hit a roadblock that I can't seem to get around. I'd greatly appreciate any pointers. I'm running R 2.6.0 on Windows XP and have Miktex 2.7 installed. I've reproduced the code below, taken from Frank Harrell's latexsummary introduction. My question relates to getting a pdf

Dear R-users: I can not reproduce figure 4 of *Statistical Tables and Plots using S and LATEX* by Prof. Frank Harrell with the following code: rm(list=ls()) library(Hmisc) getHdata(pbc) attach(pbc) age.groups <- cut2(age, c(45,60)) g <- function(y) apply(y, 2, quantile, c(.25,.5,.75)) y <- with(pbc, cbind(Chol=chol,Bili=bili)) # You can give new column names that are not legal S names

Hello, I wonder when I do coxph in R: coxph( Surv(start, stop, event) ~ x, data=test) If x is a categorical varible (1,2,3,4,5), should I creat four dummy varibles for it? if yes, how can I get the overall p value on x other than for each dummy variable? Thanks Lisa Wang Princess Margaret Hospital Phone 416 946 4501

# Dear list, # I am an R-beginner and # spent the last days looking for a method to insert tables produced # with R into a word document. I thought about SPPS: copy a table from # an SPO-file and paste it into a word document # (if needed do some formatting with that table). # Annother idea was, to produce a TEX-file, # insert it and make it a word-table. # I found the following libraries, which

Hi - I am fitting various Cox PH models with spline predictors. After fitting the model, I would like to use termplot() to examine the functional form of the fitted model (e.g., to obtain a plot of the relative risk (or log r.r.) versus the predictors). When there is only 1 predictor in the model, termplot returns a "?". In this case, I have not been able to figure out how to create

Hello, I am trying to find an R function to compute 'direct adjusted survival' with standard errors. A SAS-macro to do this is presented in Zhang X, Loberiza FR, Klein JP, Zhang MJ. A SAS macro for estimation of direct adjusted survival curves based on a stratified Cox regression model. Comput Methods Programs Biomed 2007;88:95-101. It appears that this method is not implemented in R.

I ran across this by accident when working up an example. It uses a data set from the survival package, but nothing else from there. Fails on the Intel machine shown below, and on a virtual linux instance on a newer Mac. Terry > library(survival) > library(lme4) Loading required package: Matrix > sessionInfo() R Under development (unstable) (2024-06-14 r86747) Platform:

Greetings ~ I need some assistance determining an appropriate approach to analyzing multivariate binary response data, and how to do it in R. The setting: Data from an assay, wherein 6-hours-post-fertilization zebrafish embryos (n=20, say) are exposed in a vial to a chemical (replicated 3 times, say), and 5 days later observed for the presence/absence (1/0) of defects in several organ systems

Thanks for the reproducable example. I can confirm that it fails on my machine using survival 2-37.5, the next soon-to-be-released version, The issue is with NextMethod, and my assumption that the called routine inherited everything from the parent, including the environment chain. A simple test this AM showed me that the assumption is false. It might have been true for Splus. Working this

Full_Name: Yasuhiro Nakajima Version: 2.6.1 OS: WinXP SP2 Submission from: (NULL) (202.237.255.13) Dear all, I noticed the following behaviour of ci.plot in HH Package(ver.2.1-9): > library(HH) > data(women, package="datasets") > attach(women) > ft <- lm(height~weight) > windows() > ci.plot(ft,conf.level=0.95) > windows() > ci.plot(ft,conf.level=0.999) I

Dear Sir, I am doing some work on a population of patients. About half of them are admitted into hospital with albumin levels less than 33. The other half have albumin levels greater than 33, so I stratify them into 2 groups, x and y respectively. I suspect that the average length of stay in hospital for the group of patients (x) with albumin levels less than 33 is greater than those

thunderbird just crashed, and I've never seen this before. Any idea what happened? this was in the dovecot log: 2010-12-02 13:06:40 imap-login: Info: Maximum number of connections from user+IP exceeded (mail_max_userip_connections): user=<pbc>, method=PLAIN, rip=127.0.0.1, lip=127.0.0.1, secured 2010-12-02 13:06:40 imap-login: Info: Maximum number of connections from user+IP exceeded

Hi, There is something I don't get with object of class "mle" returned by a function I wrote. More precisely it's about the behaviour of method "confint" and "profile" applied to these object. I've written a short function (see below) whose arguments are: 1) A univariate sample (arising from a gamma, log-normal or whatever). 2) A character string

DeaR experts, I was asked for a log-rank pairwise survival comparison. I've a straightforward way to do this using the SAS system: http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#/documentation/cdl/en/statug/63033/HTML/default/statug_lifetest_sect019.htm What I've found in R is shown below, but it's not a logrank test, I suppose. (The documentation