similar to: multistate survival analysis w/ time varying covariates

Hi everyone, I am trying to run the example of multistate occupancy model from the book Bayesian Population Analysis using WinBUGS (Marc Kéry and Michael Schaub): This example is available on http://www.vogelwarte.ch/code-for-running-bpa-using-jags.html When I try to run the first line of this section of the model : # Initial values zst

hello,everyone: I am now confused in multistate survival , when I want to poccess a multistate survival analysis, I turn to R and the package 'mstate' and packege 'cmprsk'. When I come to publishing the article. Follow requirement of the magzine, the statistic is carry out in LTA package (which was said to be a authority in intauterine from WHO) . There we found difference

This message was also sent to the MEDSTATS mailing list, so here is the reply I posted to that: Philippe, The machinery to use is to split follow-up time so finely that you can safely assume that rates are constant in each interval, and then just stuff it all into a Poisson model. This allows you to use any kind of time-dependent variables as well as accommodating competing risks. In the Epi

Help! I am unsure if I can analyze data from the following experiment. Fish were placed in a tank at (t=0) Measurements of Carbon Dioxide were taken each day for 120 days (t=0,...120) A few fish were then randomly pulled out of the tank at different days, killed and examined for the presence of a disease T= time of examination in days from start (i.e. 85th day), E = 0/1 for nonevent/event My

Hi! I want to estimate a shared gamma frailty model with left truncated data. I use a parametric baseline hazard so that I can use simple ML estimation. As I have a big data set it is ok to assume piecewise constant baseline hazards. As my data are left truncated I have modified the definition of the risk set. Do I also have to modifiy the frailty distribution if I have left truncated data?

Hello, coxph does not use any information that are in the dataset between event times (or "death times") , since computation only occurs at event times. For instance, removing observations when there is no event at that time in the whole dataset does not change the results: > set.seed(1) > data <- as.data.frame(cbind(start=c(1:5,1:5,1:4),stop=c(2:6,2:6,2:5),status=c(rep(

Let me expand a bit on Thomas's answer. Looking more closely at your data set you have the following: death time group 0 group 1 1.5 0/4 13/13 3 0/4 5/5 8 4/4 0 At time 1.5 group 1 had 13 deaths out of 13 at risk, group 0 had none. Time 8 doesn't have any impact on the fit, since only one group

Hi, I am a newbie to the world of R. I have a data converted in csv format. Few cells in some of the rows of the data are blank ( in the sense that there is no value available for the particular experiment). When i try to open the file in R. I get an warning message. The specific warning message i get is {Warning message: NAs introduced by coercion } My data more or less looks like this -

Hello, I am trying to model an intensity function with time-varying covariates. Before, I have successfully defined a log likelihood function for a Power-Law Process (lambda(t)=alpha*beta*t^(beta-1)) with two paramters and no covariates for a repairable systems with failure times (t). This function was maximized with R optim. No problem! But now I want to include a covariate indicating a

Hi I'm trying to use the fold function as described here: http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-cox-regression.pdf Page9 It does say that you can use this when you have more than one time varying covariate: in the description of the argument cov it says: "cov: A vector giving the column numbers of the time-dependent covariate in data, or a list of vectors if there

Dear Prof. Brostr?m, Dear R-mailinglist, first of all thanks a lot for your great effort to incorporate time-varying covariates into aftreg. It works like a charm so far and I'll update you with detailled benchmarks as soon as I have them. I have one more questions regarding Accelerated Failure Time models (with aftreg): You mention that left truncation in combination with time-varying

Good day I've got a very strange problem with AD and winbindd. What I'm trying to do is to let AD users to login into freebsd box (using pam_winbindd). I've joined domain successfully, everything works perfectly but several users cannot login. Login fails for ALL users who have 'w' in their samAccountNames, and ONLY for them. wbinfo -n swan gives:

Hi all, Our customer wants to install a VPN server on Centos 5 box. This is what the remote site says. pls read below. We now have the new firewall & router installed in our office, that supports a secure VPN connection. We need to enter your Peer IP and your Private IP in our device. They indicated xxxx.xxxx.xxxx/24 as format for the Private IP. The settings you need to know at our end

From: Sebastian Andrzej Siewior <sebastian at breakpoint.cc> The "zstd at breakpoint.cc" compression algorithm enables ZSTD based compression as defined in RFC8478. The compression is delayed until the server sends the SSH_MSG_USERAUTH_SUCCESS which is the same time as with the "zlib at openssh.com" method. Signed-off-by: Sebastian Andrzej Siewior <sebastian at

Hi, Are there any special statistical tests, or functions in R I could use to measure "cone-like" distributions? I have several data-sets, which I've been plotting parts of as 2D plots, where I get a "cone-like" distribution of the data points. That is, the data appears to be bounded by two non-parallel lines, starting at the origin, giving rise to a

I am getting a repeated error when I try to run a logistic regression in R 2.8.1 >(glm(prop1~x1,data=glm1,family=binomial("logit"),weights=nt1)) Error in model.frame.default(formula = prop1 ~ x1, data = glm1, weights = nt1, : invalid type (list) for variable 'x1' x1 is multistate categorical (3 categories). 2 of the categories have 12 observation, one has 9. Is this what

I tried to use ctree but am not sure about the meaning of the plot. My.data.ct<-ctree(Resp~., data=My.data) plot(My.data.ct) My data.frame contains 88 explanatory variables (continous,ordered/unordered multistate,count data) and one response with two groups. In the plot are only two variables shown (2 internal nodes) and 3 final nodes. Does it mean that only these two variables show a

Dear list, I want o fit a shared gamma frailty model with the frailty specification in the survival package. I have partly left-truncated data and time-dependent covariates. Is it possible to combine these two things in the frailty function. Or are the results wrong if I use data in the start-stop-formulation which account for delayed entry? Is the frailty distribution updated in the

Dear all, Let's assume I have a clinical trial with two treatments and a time to event outcome. I am trying to fit a Cox model with a time dependent treatment effect and then plot the predicted survival curve for one treatment (or both). library(survival) test <- list(time=runif(100,0,10),event=sample(0:1,100,replace=T),trmt=sample(0:1,100,replace=T)) model1 <- coxph(Surv(time,

Dear List, I am looking to perform exploratory analyses of two (relatively) large datasets of categorical data. The first one is a binary 80x100 matrix, in the form: matrix(sample(c(0,1),25,replace=TRUE), nrow = 5, ncol=5, dimnames = list(c( "group1", "group2","group3", "group4","group5"), c("V.1", "V.2", "V.3",