similar to: specials and ::

R Folk: Please forgive what I'm sure is a fairly na?ve question; I hope it's clear. A colleague and I have been doing a really simple one-off survival analysis, but this is an area with which we are not very familiar, we just happen to have gathered some data that needs this type of analysis. We've done quite a bit of reading, but answers escape us, even though the question below

ExpeRts, I'm trying to compare three survival curves using the function "survdiff" in the survival package. Following is my code and corresponding error message. > survdiff(Surv(st_months, status) ~ strata(BOR), data=mydata) Error in survdiff(Surv(st_months, status) ~ strata(BOR), data = mydata) : No groups to test When I check the "strata" of the variable. I get .

A user reported a problem with the survdiff function and the use of variables that contain a space.? Here is a simple example.? The same issue occurs in survfit for the same reason. lung2 <- lung names(lung2)[1] <- "in st"?? # old name is inst survdiff(Surv(time, status) ~ `in st`, data=lung2) Error in `[.data.frame`(m, ll) : undefined columns selected In the body of the code

This is more of a general question without data. After doing 'survdiff', from the 'survival' package, on strata including four groups (so 4 curves on a Kaplan Meier curve) you get a chi squared p-value whether to reject the null hypothesis or not. Is there a method to followup with pairwise testing on the respective groups? I have searched the library but have come up with

I recently had a long argument wrt the survival package, namely that the following code didn't do what they expected, and so they reported it as a bug ? survival::coxph( survival::Surv(time, status) ~ age + sex + survival::strata(inst), data=lung) a. The Google R style guide? recommends that one put :: everywhere b. This breaks the recognition of cluster as a "special" in the

Ben, Looking at your notes, it appears that your solution is to write your own terms() function for lme.? It is easy to verify that the "varnames.fixed" attribute is not returned by the ususal terms function. Then I also need to write my own terms function for the survival and coxme pacakges? Because of the need to treat strata() terms in a special way I manipulate the formula/terms in

Thanks to Bill Dunlap for the clarification. On follow-up it turns out that this will be an issue for many if not most of the routines in the survival package: a lot of them look at the terms structure and make use of the dimnames of attr(terms, 'factors'), which also keeps the unneeded backquotes. Others use the term.labels attribute. To dodge this I will need to create a

>>>>> Ben Bolker <bbolker at gmail.com> >>>>> on Thu, 8 Mar 2018 09:42:40 -0500 writes: > Meant to respond to this but forgot. > I didn't write a new terms() function -- I added an attribute to the > terms() (a vector of the names > of the constructed model matrix), thus preserving the information at > the point when

This arose when working on an addition to coxph, which has the features that the X matrix never has an intercept column, and we remove strata() terms before computing an X matrix. The surprise: when a terms object is subset the intercept attribute is turned back on. My lines 2 and 3 below were being executed just before a call to model.frame. The simple solution was of course to do them in the

In the long run, coming up with a way to parse specials in formulas that is both clean and robust is a good idea - annoying users are a little bit like CRAN maintainers in this respect. I think I would probably do this by testing identical(eval(extracted_head), survival::Surv) - but this has lots of potential annoyances (what if extracted_head is a symbol that can't be found in any attached

Dear All, is there a stratified version of the Wilcoxon test (also known as van Elteren test) available in R? I could find it in the survdiff function of the survival package for censored data. I think, it should be possible to use this function creating a dummy censoring indicator and setting it to not censored, but may be there is a better way to perform the test. Thanks, Heinz T??chler

How can I get the Log - Rank p value to be output? The chi square value can be output, so I was thinking if I can also have the degrees of freedom output I could generate the p value, but can't see how to find df either. > (survtest <- survdiff(Surv(time, cens) ~ group, data = surv,rho=0)) Call: survdiff(formula = Surv(time, cens) ~ group, data = surv, rho = 0) N Observed

Hi, I have a data frame: > class(B27.vec) [1] "data.frame" > head(B27.vec) AGE Gend B27 AgeOn DD uveitis psoriasis IBD CD UC InI BASDAI BASFI Smok UV 1 57 1 1 19 38 2 1 1 1 1 1 5.40 8.08 NA 1 2 35 1 1 33 2 2 1 1 1 1 1 1.69 2.28 NA 1 3 49 2 1 40 9 1 1 1 1 1 1 8.30 9.40 NA

Dear all, What is the cumulative distribution (with parameterization) used within survreg with respect to the log-logistic distribution? That is, how are the parameters linked to the survivor function? Best regards, Mario [[alternative HTML version deleted]]

--- included message ---- Thus, my question is: *What common measures exists for ranking/measuring variable importance of participating variables in a CART model? And how can this be computed using R (for example, when using the rpart package)* ---end ---- Consider the following printout from rpart summary(rpart(time ~ age + ph.ecog + pat.karno, data=lung)) Node number 1: 228 observations,

I read through Harrington and Fleming (1982) but it is beyond my statistical comprehension. I have survival data for insects that have a very finite expiration date. I'm trying to test for differences in survival distributions between different groups. I understand that the medical field is most often dealing with censored data and that survival analysis, at least in the package survival,

Dear all, I have a question with respect to counting process formulation of the coxph(survival) model. I have two groups of observations for which I have partitioned each observation into two distinct time intervals, namely, entry day till day 13, and day 13 till death or censorship day (of course the latter only for the observations that survived the first 13 day interval), and added a

Terry - your example didn't demonstrate the problem because the variable that interacted with strata (zed) was not a factor variable. But I had stated the problem incorrectly. It's not that there are too many strata terms; there are too many non-strata terms when the variable interacting with the stratification factor is a factor variable. Here is a simple example, where I have

Terry - your example didn't demonstrate the problem because the variable that interacted with strata (zed) was not a factor variable. But I had stated the problem incorrectly. It's not that there are too many strata terms; there are too many non-strata terms when the variable interacting with the stratification factor is a factor variable. Here is a simple example, where I have

I am confused when trying the function survfit. my question is: what does the survival curve given by plot.survfit mean? is it the survival curve with different covariates at different points? or just the baseline survival curve? for example, I run the following code and get the survival curve #### library(survival) fit<-coxph(Surv(futime,fustat)~resid.ds+rx+ecog.ps,data=ovarian)