similar to: survival analysis and censoring

Dear UseRs, Suppose I have data regarding smoking habits of a prospective cohort and wish to determine the risk ratio of colorectal cancer in the smokers compared to the non-smokers. What do I do at the end of the study with people who die of heart disease? Can I just censor them exactly the same as people who become uncontactable or who die in a plane crash? If not, why not? I'm thinking

Dear all I have a fairly funky problem that I think demands some sort of survival analysis. There are two Red List assessments for mammals: 1986 and 2008. Some mammals changed their Red List status between those dates. Those changes can be regarded as "events" and are "interval censored" in the sense that we don't know at what point between 1986 and 2008 each species

Hi there, can somebody give me a guide as to how to generate data from weibull distribution with censoring for example, the code below generates only failure data, what do i add to get the censored data, either right or interval censoring q<-rweibull(100,2,10). Thank you Grace Kam student, University of Ghana [[alternative HTML version deleted]]

Dear all (not an R question per se, but given that the Real pRo's are all heRe I hope you foRgive) survival analyses assume that censoring is independent of hazard etc (eg, MASS 4th ed, pg. 354). Is there a standard test for this assumption? If there is not, what would you do to examine it empirically? (over and above some thinking about how censoring might be related to baseline factors).

I would like to do some power estimations for a log-rank two sample test and cpower seems to fit the bill. I am getting confused though by the man page and what the arguments actually mean. I am also not sure whether cpower takes into account censoring or not. Could anyone provide a simple example of how I would get the power for a set control/non-control clinical trial where censoring occurs at

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

Hello, I would like to know if there is a function in R that will test for normality and handle censored data sets. Currently, I evaluate each censored data set by the extent to which a normal scores plot approximate a straight line. For complete data sets I use shapiro.test(). Below is an example of a censored data set. data1<-c(0.00, 0.00, 0.00, 5.86, 5.17, 8.17, 5.12, 4.92, 7.08,

List, Consider the following data. gender mygroup id 1 F A 1 2 F B 2 3 F B 2 4 F B 2 5 F C 2 6 F C 2 7 F C 2 8 F D 2 9 F D 2 10 F D 2 11 F D 2 12 F D 2 13 F D 2 14 M A 3 15 M A 3 16 M A 3 17

Does anybody know if there is such a function to estimate the distribution for interval censored data? survfit doesn't work for this type of data as I tried various references. [[alternative HTML version deleted]]

Hi, I have a right-censored (positive) random variable (e.g. failure times subject to right censoring) that is observed for N subjects: Y_i, I = 1, 2, ..., N. Note that Y_i = min(T_i, C_i), where T_i is the true failure time and C_i is the censored time. Let us assume that C_i is independent of T_i. Now, I would like to generate another random variable U_i, I = 1, 2, ..., N, which is

Hi, I'm trying to figure out if there are any packages allowing one to fit mixed models (or non-linear mixed models) to data that includes censoring. I've done some searching already on CRAN and through the mailing list archives, but haven't discovered anything. Since I may well have done a poor job searching I thought I'd ask here prior to giving up. I understand that

Hi, when ploting Kaplan-Meier estimate curves as below, the censoring symbols (crosses) to not change thickness along the lines plot(survfit(surv ~ I(x>=cut.off) ),lty=c(1,2), lwd=2) is there any strightforward way to make it happen? thanks robert -- View this message in context: http://r.789695.n4.nabble.com/censoring-symbols-on-survfit-plot-tp3311283p3311283.html Sent from the R help

Dear r-helpers, This is my first time to run survival analysis. Currently, I have a data set which contains two variables, the variable of time to event (or time to censoring) and the variable of censor indicator. For the indicator variable, it was coded as 0 and 1. 0 represents right censor, 1 means event of interest. Now I try to use "survfit" in the package of "survival". I

First let me admit that I am no statistician... rather, an ecologist with just enough statistical knowledge to be dangerous. I've got a dataset with percent ground cover values for species and other entities. The data are left censored at zero, in that percent ground cover cannot be negative. (My data rarely reach 100% cover so I haven't bothered with adding a right censoring at 100).

Your function is giving NaN's during the optimization. The R-forge version of optimx() has functionality specifically intended to deal with this. NOTE: the CRAN version does not, and the R-forge version still has some glitches! However, I easily ran the code you supplied by changing optim to optimx in the penultimate line. Here's the final output. KKT condition testing Number of

Dear all, I am interested to evaluate reclassification using net reclassification improvement and Integrated Discrimination Index IDI after survival analysis (Cox proportional hazards using stcox). I search a R package or a R code that specifically addresses the categorical NRI for time-to-event data in the presence of censored observation and, if possible, at different follow-up time points. I

Hello, I am having some trouble with the 'censoring-adjusted C-index' by Uno et al, in the package survAUC. The relevant function is UnoC. The question has to do with what happens when I specify a time point t for the upper limit of the time range under consideration (we want to avoid using the right-end tail of the KM curve). Copying from the example in the help file: TR <-

I will have data in the following form: Time resp type stim type 300 a A 200 b A 155 a B 250 b B 80 c A 1000 d B ... c is left censored observation; d is right censored This sort of problem is discussed in Chap 9 of Cox & Oakes Analysis of Survival Data under the name

I have a question about constructing the likelihood function where there is censoring at level 1 in a two-level random effects sum. In a conventional solution, the likelihood function is constructed using the density for failures and the survivor function for (in this case, right) censored results. Within (for example) an R environment, this is easy to do and gives the same solution as survreg

Hi everybody, recently I had to teach a course on Cox model, of which I am not a specialist, to an audience of medical epidemiologists. Not a good idea you might say.. anyway, someone in the audience was very hostile. At some point, he sayed that Cox model was useless, since all you have to do is count who dies and who survives, divide by the sample sizes and compute a relative risk, and if there