search for: obstim

...pages={245-252} } ######################? Code to simulate survival data under complex situations (MacKenzie and Abrahamowicz, Statistics and Computing 2002) ####################### #sample size n<-100 R<-1:n #explanatory variable x<-runif(n,3,9) #survival time and censoring variable: obsTime<-rexp(n,1) status<-ifelse(runif(n)>.2,1,0) obsTime<-obsTime[order(obsTime)] status<-status[order(obsTime)] dati<-matrix(-99,n,(1+2)) #hazard ratio as function of the explanatory variable xx, and survival time. HR<-function(ttime,xx){exp(.5)} #.5 is the log hazard ratio P<...

...atistics/chapter16.html (Primary Biliary Cirrhosis data link at top of the page), I'm using the following code: --------------- start of code library(survival) liver <- scan("liver2.txt",list(age=0,albumin=0,alkphos=0,ascites=0,bili=0, cholest=0,edema=0,edmadj=0,hepmeg=0,obstime=0,platelet=0,protime=0, sex=0,sgot=0,spiders=0,stage=0,status=0,treatmnt=0, triglyc=0,urinecu=0)) fit<-coxph(Surv(obstime,status)~bili+edmadj+albumin+protime+age,data=liver) summary(fit) ----------------- End of code but the answer is rather different from that in the book (p.6...

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 2007-10-30 05:08:22 5261 fred 0.1 0.2 99.7 2007-10-25 07:20:32 12427 barney 0.1 0.2 93.2 2007-10-19 14:34:10 18067 barney 0.1 0.2 99.4 2007-10-27 10:34:08 973...

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

...son 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, "offset" in Poisson regression): obs obstime cmin cmax 1 1 2 Inf 2 2 0 1 3 1 1 2 and so on. Best wishes, Justine --

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