similar to: Use of Lexis function to convert survival data to counting format

To Danardonos concern of splitting time for records with delayed entry: This can fairly easily be accomodated, by simply splitting time in small intervals of time since entry into the study, and then compute the value of the other timescales for each of these e.g.: current.age <- time.from.entry + age.at.entry but the cut on the other timescales will not be exactly where you may want them

Dear R-users, Sorry for bothering you twice in a day, but I was wondering whether there is any R-function which can easily plot the single elements of a Lexis diagram. I though that Lexis.diagram(Epi) could be the case, but it just plot life times in a frame. In particular I have been searching for something similar to the function "image" which can plot either the single triangles of a

Hi, I am trying to simulate a regression on survival data under a few conditions: 1. Under different error distributions 2. Have the error term be dependent on the covariates But I'm not sure how to specify either conditions. I am using the Design package to perform the survival analysis using the survreg, bj, coxph functions. Any help is greatly appreciated. This is what I have so far:

Dear All, since years I am struggling with Surv objects in data.frames. The following seems to have to do with it. See below the modified example from the help page of survSplit. The original works, as expected. If, however, a Surv object is added to the data.frame, each record gets doubled. Is there some solution other than avoiding Surv objects in data.frames? Thanks, Heinz

i would like to add a variable to an existing matrix by manipulating 2 previous variables eg for the data m treat strata censti survTime [1,] 1 2 284.684074 690.4961005 [2,] 1 1 172.764515 32.3990335 [3,] 1 1 2393.195400 24.6145279 [4,] 2 1 30.364771 8.0272267 [5,] 1 1 523.182282 554.7659501 l

hie l would like to create a 6th column "actual surv time" from the following data the condition being if censoringTime>survivaltime then actual survtime =survival time else actual survtime =censoring time the code l used to create the data is s=2 while(s!=0){ n=20 m<-matrix(nrow=n,ncol=4)

Dear R-users, I use the survsplit function in the survival package to change my data into counting-process format and the transformed format is as follow: (a) start stop event DP age .... 0 5 0 1 20 5 10 0 1 20 10 25 1 1 20 looking at the above three entries that belong to the same person, if an event happen at

Dear All, I got a warning message "X matrix deemed to be singular" in Cox model with a time dependent coefficient. In my analysis, the variable "SEX" is a categorical variable which violate the PH assumption in Cox. I first used the survSplit() function to break the data set into different time intervals, and then fit the model. The procedures can be described as follows:

Dear R users, I am doing Cox regression using coxph(Survival) or cph(Design). I have time varying effects (diagnosed with schoenfeld residuals Chi2 test and graph) so I first want to split time into 2 separate intervals : t<6months and t>=6months, to estimate one hazard ratio (hr) for each interval. I am analysing Overall survival according to 3 prognostic factors (age,deep,ldh).

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

A few days ago I uploaded to CRAN a new package called 'eha', which stands for 'Event History Analysis'. Its main focus is on proportional hazards modeling in survival analysis, and in that respect eha can be regarded as a complement and an extension to the 'survival' package. In fact eha requires survival. Eha contains three functions for proportional hazards

A few days ago I uploaded to CRAN a new package called 'eha', which stands for 'Event History Analysis'. Its main focus is on proportional hazards modeling in survival analysis, and in that respect eha can be regarded as a complement and an extension to the 'survival' package. In fact eha requires survival. Eha contains three functions for proportional hazards

To the R developers, The discussion below reminded me that I think it might be a good idea to take the Relevel function from the Lexis package and replace relevel in stats with it. This is really nothing special for epidemiology. It is fully compatible with the existing relevel (it actually contains the relevel code almost verbatim as a subset), but it has the extra functionality of combining

I am designing an interlaboratory validation study for a presence/absence alternative method test kit vs. a presence/absence reference method test kit. There will be 10 laboratories conducting tests using both methods. In each laboratory, there will be 5 specimens tested, each of the 5 specimens twice by both methods (alternative, standard). The total number of data are 10 x 5 x 4 = 200.

I need to be able to run a generalized linear model with a log() link and a Weibull family, or something similar to deal with an extreme value distribution. I actually have a large dataset where this is apparently necessary. It has to do with recovery of forensic samples from surfaces, where as much powder as possible is collected. This apparently causes the results to conform to some type

Another sample problem: In the Windows version of R-2.5.0, data(GHQ,package='HSAUR') layout(1) GHQ_glm_1<- glm(cbind(cases,non.cases) ~ GHQ, data=GHQ, family=binomial()) summary(GHQ_glm_1) yfit_glm_1<- predict(GHQ_glm_1, type='response') layout(1) plot(probs ~ GHQ,pch=1,col=1,ylab='Probability(Case)', data=GHQ) lines(yfit_glm_1 ~ GHQ, pch=3,col=3, data=GHQ)

[Corrected the model formula to include "method".] I am designing an interlaboratory validation study for a presence/absence alternative method test kit vs. a presence/absence reference method test kit. There will be 10 laboratories conducting tests using both methods. In each laboratory, there will be 5 specimens tested, each of the 5 specimens twice by both methods (alternative,

Are there any packages in R that reproduce the package "resample" of S-Plus? The sample() function in R doesn't provide equivalent flexibility of bootstrap() and bootstrap2(). ================================================================ Robert A. LaBudde, PhD, PAS, Dpl. ACAFS e-mail: ral at lcfltd.com Least Cost Formulations, Ltd. URL: http://lcfltd.com/ 824

I am trying to compute bootstrap confidence intervals for a sample using R 2.5.1 for Windows. I can get "Normal", "Basic", "Percentile", "BCa" and "ABC" from boot.ci() and boot() in the Davison & Hinkley "boot" package. But I can't figure out how to use tilt.boot() to get the "tilting" confidence interval.

Given a set of data with a number of variables plus a response, I'd like to obtain a randomized subset of the rows such that the marginal proportions of each variable are maintained closely in the subset to that of the dataset, and possibly maintaining as well the two-factor interaction marginal proportions as well for some pairs. This must be a common problem in data mining, but I