similar to: Comparing survival curves with "survdiff" "strata" help

Displaying 20 results from an estimated 1000 matches similar to: "Comparing survival curves with "survdiff" "strata" help"

2012 Oct 19
2
Question about survdiff in for-loop.
Hi everyone!! I have dataset composed of a numbers of survival analyses. ( for batch survival analyses by using for-loop) . Here are code !! ####### dim(svsv) Num_t<-dim(svsv) Num<-Num_t[2] # These are predictors !! names=colnames(svsv) for (i in 1:Num ) { name_tt=names[i] survdiff(Surv(survival.m, survival) ~ names[i], data=svsv) fit.Group<-survfit(Surv(survival.m, survival) ~
2001 Nov 22
1
p-value using survdiff
Dear all, Does anyone knows how I could extract the p-value in: > survdiff(Surv(tempo,status) ~ grupo,data=dados1,rho=1) Call: survdiff(formula = Surv(tempo, status) ~ grupo, data = dados1, rho = 1) N Observed Expected (O-E)^2/E (O-E)^2/V grupo=1 21 5.12 12.00 3.94 14.5 grupo=2 21 14.55 7.68 6.16 14.5 Chisq= 14.5 on 1 degrees of freedom,
2012 Oct 18
1
looping survdiff?
Hello, I am trying to set up a loop that can run the survdiff function with the ultimate goal to generate a csv file with the p-values reported. However, whenever I try a loop I get an error such as "invalid type (list) for variable 'survival_data_variables[i]". This is a subset of my data: structure(list(time = c(1.51666666666667, 72, 72, 25.7833333333333, 72, 72, 72, 72, 72,
2007 Apr 26
2
Extract p-value from survdiff function
Hi list, I want to use the p-value from the survdiff function (package survival) to reuse within a function in a Kaplan-Meier plot. The p-value is somehow not a component of the value list ?! Thanks in advance -- A. Goralczyk G?ttingen, Ger.
2009 Sep 16
2
Teasing out logrank differences *between* groups using survdiff or something else?
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
2012 Oct 19
1
Looping survdiff
The number of recent questions from umn.edu makes me wonder if there's homework involved.... Simpler for your example is to use get and subset. dat <- structure(..... as found below var.to.test <- names(dat)[4:6] #variables of interest nvar <- length(var.to.test) chisq <- double(nvar) for (i in 1:nvar) { tfit <- survdiff(Surv(time, completion==2) ~
2010 Jul 07
1
Appropriateness of survdiff {survival} for non-censored data
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,
2004 Aug 17
1
survdiff
Hello, As I am quitte an ignorant user of R, excuse me for any wrongfull usage of all the terms. My question relates to the statistics behind the survdiff function in the package survival. My textbook knowledge of the logrank test tells me that if I want to compare two survival curves, I have to take the sum of the factors: (O-E)^2/E of both groups, which will give me the Chisq. If I calculate
2005 Nov 23
1
survdiff for Left-truncated and right-censored data
dear all, I would like to know whether survdiff and survReg function in the survival package work for left-truncated and right-censored data. If not, what other functions can i use to make comparison between two survival curves with LTRC data. thanks for any help given sing yee
2013 Apr 29
3
Comparing two different 'survival' events for the same subject using survdiff?
I have a dataset which for the sake of simplicity has two endpoints. We would like to test if two different end-points have the same eventual meaning. To try and take an example that people might understand better: Lets assume we had a group of subjects who all received a treatment. The could stop treatment for any reason (side effects, treatment stops working etc). Getting that data is very
2004 Sep 28
2
Validating a Cox model on an external set
Good morning, Sorry to trouble the list. I have a problem I hope to seek your advice on. Essentially, I am trying to 'validate' a multivariate Cox proportional hazards model built in a training set, by testing it on an external test set. I have performed a survfit using the Cox model to predict survival for the test set, and obtained individual predictions for survival time, with
2009 Aug 03
1
survdiff for left-truncated data?
Hi Does anyone know if there is a function like survdiff which can also handle left-truncated and right-censored data? When I use it on left-truncated and right-censored data I get an error message saying Right censored data only. Many thanks Rajen [[alternative HTML version deleted]]
2012 Feb 23
2
Survival analysis and comparing survival curves
Hei, I have a one simple question which does not seem to be that simple as I cannot find any solution/answer: Is it possible to compare multiple survival curves in R with survdiff-function when there is interaction term involved in predictor variables (and this interaction is significant)? Example: survdiff(Surv(death,status)~treatment*gapsize) R is making "problems" with it ie.e.
2011 Jul 21
0
Survdiff for multiple comparisons
Hello all- I am doing a survival analysis for two species of invasive plants I outplanted to edges and interiors of island and mainland sites in a local reservoir. I am using the KM estimate and had no problem doing survdiff for my data using the following code: S4<-Surv(outplant$SurvTime, outplant$StatusD6) diff4=survdiff(S4 ~ outplant$Species+outplant$SiteType+outplant$EdgInt) diff4
2007 May 16
2
log rank test p value
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
2018 Feb 15
0
Fleming-Harrington weighted log rank test
> On Feb 13, 2018, at 4:02 PM, array chip via R-help <r-help at r-project.org> wrote: > > Hi all, > > The survdiff() from survival package has an argument "rho" that implements Fleming-Harrington weighted long rank test. > > But according to several sources including "survminer" package
2012 Jan 26
2
extracting from data.frames for survival analysis
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
2018 Feb 15
1
Fleming-Harrington weighted log rank test
> On Feb 14, 2018, at 5:26 PM, David Winsemius <dwinsemius at comcast.net> wrote: > >> >> On Feb 13, 2018, at 4:02 PM, array chip via R-help <r-help at r-project.org> wrote: >> >> Hi all, >> >> The survdiff() from survival package has an argument "rho" that implements Fleming-Harrington weighted long rank test. >>
2018 Feb 14
2
Fleming-Harrington weighted log rank test
Hi all,? The survdiff() from survival package has an argument "rho" that implements Fleming-Harrington weighted long rank test.? But according to several sources including "survminer" package (https://cran.r-project.org/web/packages/survminer/vignettes/Specifiying_weights_in_log-rank_comparisons.html), Fleming-Harrington weighted log-rank test should have 2 parameters
2016 Aug 11
3
Comparación de probabilidades de supervivencia en R
Estimados miembros de la lista, Estoy haciendo una análisis de supervivencia con R. Adjunto mis datos. Quiero analizar la supervivencia de 5 grupos diferentes y compararla. Para ello estoy utilizando el paquete survival. > s = Surv(c$tiempo, c$estado) > f = survfit(s ~ tratamiento, data = c) > d = survdiff(s ~ tratamiento, data = c) > d Call: survdiff(formula = s ~ tratamiento,