search for: breslow

Hi all, The survivals functions can be tested by the Log-rank test and others, for example the Gehan-Breslow. The graham breslow work with the alpha values. But I don't know how is the Gehan-Breslow test with R. Somebody know a type function?.. or other suggestions? Any help will be really appreciated Jos? Bustos Marine Biologist Master Apllied Stat Program University of Concepci?n

Breslow-Day test A statistical test for the homogeneity of odds ratios. Homogeneity In <javascript:void(0);> systematic reviews homogeneity refers to the degree to which the results of studies included in a review are similar. "Clinical homogeneity" means that, in studies included in...

Dear all, I have been trying to find a function to calculate the Breslow-Day test for homogeneity of the odds ratio in R. I know the test can be preformed in SAS but i was wondering if anyone could help me to perform this in r. In addition i have the fullrefman file to search for functions in the basic R packages, does anyone have any suggestions of an efficient way...

Hi all, I want to know how to perform the test Breslow-Day test for homogeneity of odds ratios (OR) stratified for svytable. This test is obtained with the following code: epi.2by2 (dat = daty, method = "case.control" conf.level = 0.95, units = 100, homogeneity = "breslow.day", verbose = TRUE) where "daty" is the...

...stratum versus time should be approximately parallel. i.e fit<-coxph(Surv(start,end,status)~sx+rated+AGLEVEL+strata(Z1),data=ALLDPinfectionandbronchitis) ss<-survfit(fit) plot(ss,fun="cumhaz") My question is on whether the cumulative hazard given by the above command is actually a breslow estimator for baseline cumulative hazard ,i.e, estimator=sum( number of death/ (sum(risk score in risk set)) or a nelson-Aalen estimator. if the above command does not give me breslow estimator, please advise on how I can get it. Thanks for any help given. kind regards, sing yee [[alternati...

Hello everyone, Does anyone know if I can do Breslow Day Test for the homogeneity of odds ratio in R? Thanks! - Jacqueline

Dear R Users, I'm looking for a package that allows to test hypothesis about a homogeneity of odds ratio in k 2x2 tables. I know that Breslow-Day is suitable but does anybody could me point out a package? I found diffR, but as far as I see this package is for IRT theory. Best, Robert

  Hi there,   I am beginner in R and I have some basic question. Suppose I run a common procedure such as a t test or cox model like below:   out<-coxph( Surv(tstart,tstop, death1) ~ x1+x1:log(tstop+1) , test1,method=c("breslow"))    Which yields the following result:   Call: coxph(formula = Surv(tstart, tstop, death1) ~ x1 + x1:log(tstop +     1), data = test1, method = c("breslow"))                    coef exp(coef) se(coef)     z    p x1                -9.58  6.89e-05     6.83 -1.40 0.16 x1:log(tstop +...

...ession has for anything branded with the word "exact", I currently see no reason to ever use that particular computation in a Cox model. I'm not quite ready to remove the option from coxph, but certainly am not going to devote any effort toward improving that part of the code. The Breslow approximation is less accurate, but is the easiest to program and therefore was the only method in early Cox model programs; it persists as the default in many software packages because of history. Truth be told, unless the number of tied deaths is quite large the difference in results between it a...

Dear all, I am struggling with calculation of Schoenfeld residuals of my Cox Ph models. Based on the formula as attached, I calculated the Schoenfeld residuals for both non tied and tied data, respectively. And then I validated my results with R using the same data sets. However, I found that my results for non-tied data was ok but the results for tied data were different from R's. How

We occasionally utilize the coxph function in the survival library to fit multinomial logit models. (The breslow method produces the same likelihood function as the multinomial logit). We then utilize the predict function to create summary results for various combinations of covariates. For example: mod1<-coxph(Depvar~Price:Product+strata(ID),data=MyDCMData2,na.action=na.omit,method="breslow")...

...ean? 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) plot(survfit(fit,type="breslow")) summary(survfit(fit,type="breslow")) #### for the first two failure points, we have s(59|x1)=0.971, s(115|x2)=0.942 how can we guarantee that s(59|x1) is always greater than s(115|x2)? since s(59|x1)=s_0(59)^exp(\beta'x1) and s(115|x2)=s_0(115)^exp(\beta'x2), we can manip...

...ittle puzzled as STATA and R seem to be yielding different survival estimates for the same dataset when treating a variable as ordinal. Ordered() is used to represent an ordinal variable) I understand that R's coxph (by default) uses the Efron approximation, whereas STATA uses (by default) the Breslow. but we did compare using the same approximations. I am wondering if this is a result of how coxph manages an ordered factor? Essentially, this is a survival dataset using tumor grade (1, 2, 3 and 4) as the risk factor. This is more of an 'ordinal' variable, rather than a continuous variab...

...a bit less obvious how to properly combine multiple variables (like stage and grade) - examples I've found aren't very helpful. For instance, can someone explain what these two function calls are doing differently? cox.model1 <- coxph(Surv(time,outcome)~stage+grade,method="breslow") cox.model2 <- coxph(Surv(time,outcome)~stage+grade +stage:grade,method="breslow") Also, am I right to think that this is how I would plot the survival curves for stage in a 50 year old patient? cox.model3 <- coxph(Surv(time,outcome)~stage+I (age-50),method="b...

...fter I simulate Time and Censor data vectors denoting the censoring time and status respectively, I can call the following function to fit the data into the Cox model (a is a data.frame containing 4 columns X1, X2, Time and Censor): b = coxph (Surv (Time, Censor) ~ X1 + X2, data = a, method = "breslow"); Now the purpose of me doing simulation is that I have another mechanism to generate the number b2. From the given b2 (say it's 4.3), Cox model can be fit to generate b1 and check how feasible the new model is. Thus, my question is, how do I specify such a model that is partially comple...

...0 14.84 2 1 2 0 1 1 08.63 A complication here is that units can experience repeated events while previous events are still ongoing. I tried the following: cox1 <- coxph( Surv( dur0, dur1, event) ~ strata(eventn) + x) This works fine under the breslow and efron method. However, since I have a fair number of ties, especially of repeated events while previous events are still ongoing, the exact method seems advisable. The help says that the exact method is computationally demanding, but even after days the computing it won't finish. Also, if...

Dear R Help list, My question is regarding extracting the standard error or Z-score from a cph or coxph call. My Cox model is: - modz=cph(Surv(TSURV,STATUS)~RAGE+DAGE+REG_WTIME_M+CLD_ISCH+POLY_VS, data=kidneyT,method="breslow", x=T, y=T) I've used names(modz) but can't see anything that will let me extract the Z scores for each coefficient or the standard errors in the same way one can use coef (modz). I need to get either the se or Zscore as I am hoping to be able to use the Zscore within a statistic fun...

...assumptions. This is where I go wrong, but I'm not sure where. Non-Stratified Nomogram: f<-cph(S~A+B+C+D+E+F+H,x=T,y=T,surv=T,time.inc=10*12,method="breslow") srv=Survival(f) srv120=function(lp) srv(10*12,lp) quant=Quantile(f) med=function(lp) quant(.5,lp) at.surv=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9) at.med=c(120,80,60,40,30,20,15,10,8,6,4,2,0) nomogram(f,lp=F, fun=list(srv120, med),funlabel=c("120-mo Survival","Median Survival&qu...

...re I summarize the software, I note that there are several ways of doing statistical inference for generalized linear mixed models: (1)Standard maximum likelihood estimation, computationally intensive due to intractable likelihood function (2) Penalized quasi likelihood or similar approximations (Breslow & Clayton, 1993; Lee & Nelder, 1999, Schall 1991). This method is not good for binomial outcomes in particular. Theoretically, it is consistent when the cluster size increases. It is not consistent when the number of clusters increases while the size of each cluster remain constant. The sta...

Hi, I am computing the Nelson-Aalen (NA) estimate of baseline cumulative hazard in two different ways using the "survival" package. I am expecting that they should be identical. However, they are not. Their difference is a monotonically increasing with time. This difference is probably not large to make any impact in the application, but is annoyingly non-trivial for me to just