search for: obstimes

Dear all, I'm looking for someone that help me to write an R function to simulate survival data under complex situations, namely time-varying hazard ratio, marginal distribution of survival times and covariates. The algorithm is described in the reference below and it should be not very difficult to implement it. However I tried but without success....;-( Below there the code that I used; it

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 <-

R-Helpers, I'm sorry to have to ask this -- I've not used R very much in the last 8 or 10 months, and I've gotten rusty. I have the following (ff2 is a subset of a much, much larger dataset): > ff2 hostName user sys idle obsTime 10142 fred 0.4 0.5 98.0 2007-11-01 02:02:18 16886 barney 0.5 0.2 94.6 2007-10-25 19:12:12 8795 fred 0.0 0.1 99.8

Hi all: I am a user of "JM" package. Here's the problem of "sample size". The warning is: Error in jointModel(fitLME, fitSURV_death, timeVar = "time", method = "piecewise-PH-GH") : sample sizes in the longitudinal and event processes differ. According to the suggestion of "missing data",I use the same data set(data_JM) without any

Dear list, I am wondering whether R allows to perform a Poisson regression on counting data which include censored observations, that is, observations of the form "2 events or more" or "less than 2 events" or even "1 or 2 events". Can anyone give me a hint whether this is already possible and how to do it? Data of the form (obstime is the observation time,

Hi, I have a system of ODE's I can solve with lsoda. Model=function(t,x,parms) { #parameter definitions lambda=parms[1]; beta=parms[2]; d = parms[3]; delta = parms[4]; p=parms[5]; c=parms[6] xdot[1] = lambda - (d*x[1])- (beta*x[3]*x[1]) xdot[2] = (beta*x[3]*x[1]) - (delta*x[2]) xdot[3] = (p*x[2]) - (c*x[3]) return(list(xdot)) } I want