similar to: Question: Self selection bias and censoring in R

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

I have to the treatment effect base on the observational data.And the treatment variable is multinomial rather than binary.Because the treatment assignment is not random,so the selection-bias exists.Under this condition,what's the best way to estimate the treatment effect? I know that if the treatment is binary,I can use propensity score matching using MatchIt package.But what about

Hi all, i am wondering if there?s any other method to adjust for selection related bias of estimates except propensity scoring and heckit / mills ratio approach? i also read documentation of Match and twang package so far, so i don?t speak of any ATE / ATT related methods, respectively any methods that match or stratify... Is there something else ? thx in advance

All, I am working with Emergency Department (ED) Length of Stay Data. The ED visit can end in one of a variety of ways (Admit, discharge, transfer, etc...) Initially, I have modeled the time to event by fitting a survival model to the time the outcome of interest and treat all other outcomes as censoring. However I recently came across the cmprsk package in R which seems to be developed

In your particular case I don't think that censoring is an issue, at least not for the reason that you discuss. The basic censoring assumption in the Cox model is that subjects who are censored have the same future risk as those who were a. not censored and b. have the same covariates. The real problem with informative censoring are the covaraites that are not in the model; ones that

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 (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).

> From: Duncan Murdoch <murdoch.duncan at gmail.com> > Let's try it: > > > m <- (2/5)*2^32 > > m > 2^31 > [1] FALSE > > x <- sample(m, 1000000, replace = TRUE) > > table(x %% 2) > > 0 1 > 399850 600150 > > Since m is an even number, the true proportions of evens and odds should > be exactly 0.5.

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]]

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

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, 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-users I have some datasets, all left-censoring, and I would like to fit distributions to (weibull,exponential, etc..). I read one solution using the function survreg in the survival package. i.e survreg(Surv(...)~1, dist="weibull") but it returns only the scale parameter. Does anyone know how to successfully fit the exponential, weibull etc... distributions to left-censoring

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

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

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 <-

On 19/09/2018 12:23 PM, Philip B. Stark wrote: > No, the 2nd call only happens when m > 2**31. Here's the code: Yes, you're right. Sorry! So the ratio really does come close to 2. However, the difference in probabilities between outcomes is still at most 2^-32 when m is less than that cutoff. That's not feasible to detect; the only detectable difference would happen if

hi i want to analyze Right Censore-Length bias data under cox model with covariate. what is the package ? tank you. [[alternative HTML version deleted]]

On 19/09/2018 3:52 PM, Philip B. Stark wrote: > Hi Duncan-- > > Nice simulation! > > The absolute difference in probabilities is small, but the maximum > relative difference grows from something negligible to almost 2 as m > approaches 2**31. > > Because the L_1 distance between the uniform distribution on {1, ..., m} > and what you actually get is large, there

For a well-tested C algorithm, based on my reading of Lemire, the unbiased "algorithm 3" in https://arxiv.org/abs/1805.10941 is part already of the C standard library in OpenBSD and macOS (as arc4random_uniform), and in the GNU standard library. Lemire also provides C++ code in the appendix of his piece for both this and the faster "nearly divisionless" algorithm. It would be