similar to: adjusting survival using coxph

Hello, I would like to compare two different models in the framework of Cox proportional hazards regression models. On Rsitesearch and google I don't find a clear answer to my question. My R-Code (R version 2.9.0) coxph.fit0 <- coxph(y ~ z2_ + cluster(as.factor(keys))+ strata(stratvar_), method="breslow" ,robust=T ) coxph.fit1 <- coxph(y ~ z_ +

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:

I'm trying to estimate a cox proportional hazards regression for repeated events (in gap time) with time varying covariates. The dataset consists of just around 6000 observations (lines) (110 events). The (stylized) data look as follows: unit dur0 dur1 eventn event ongoing x 1 0 1 0 0 0 32.23 1 1 2 0 1 1 35.34 1

Hi, I was wondering if it would be possible to print the counts of common items in the venneuler() diagrams. Anybody knows a straightforward idea? I can always count things from the data matrix, but I have vectors with 50 thousands elements. thanks Francesco -- -------------------------------------- *Francesco Lescai, PhD* Research Associate in Genome Analysis Faculty of Biomedical Sciences,

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

Hi folks, Can someone please help me in identifying the type of plot shown here? Sample R code or specific package name would be of help as well. Thanks, -Girish http://www.nabble.com/file/p24477714/example_plot.jpg -- View this message in context: http://www.nabble.com/Help-needed-in-identifying-type-of-plot-tp24477714p24477714.html Sent from the R help mailing list archive at Nabble.com.

Hello, I am currently trying to simulate data and analyze it using the frailty option in the coxph function. I am working with recurrent event data, using counting process notation. Occasionally, (about 1 in every 100 simulations) I get the following warning: Error in coxph(Surv(start, end, censorind) ~ binary + uniform + frailty(subject, : X matrix deemed to be singular; variable 2 My

Dear all, I have a question concerning the p-value. When running coxph I get a p-value = 0. :confused: Can this be true? Why aren?t there decimal points? Is there a way to find out the exact p-value? Here is the output: ---------------------------------------------------------------------------------------------------- Call: coxph(formula = Surv(start, stop, status) ~ Albumin_gproL, data = dial,

Hi all, I'm wanting to be able to find and store the z-score of coxph below: - modz=coxph(Surv(TSURV,STATUS)~RAGE+DAGE+REG_WTIME_M+CLD_ISCH+POLY_VS, data=kidneyT,method="breslow") I know summary(modz) will give me this, but how do i extract the standard error or z-score values in a similar way to obtaining the coefficients by coef(modz) ? I think it must be something to do with

Dear R-help members, Apologies - I am posting on behalf of a colleague, who is a little 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

Would anybody be able to advise on which package would offer the best approach for producing a model able to predict the probability of species occupation based upon a range of variables, some of them catagorical (eg. ten soil types where the numbers assigned are not related to any qualitative/quantitative continuum or vegetation type) and others continuous such as field size or vegetation height.

--- begin inclusion -- After 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

Dear all, I have a couple of questions regarding the survival:::cch function. 1) I notice that Prentice and Self-Prentice functions are giving identical standard errors (not by chance but by programming design) while their estimates are different. My guess is they are both using the standard error form from Self and Prentice (1986). I see that standard errors for both methods are

Works perfectly. Thank you. -Hugh Rand -----Original Message----- From: Spencer Graves [mailto:spencer.graves at pdf.com] Sent: Sunday, January 15, 2006 6:41 PM To: Rand, Hugh Cc: 'r-help at lists.R-project.org' Subject: Re: [R] trouble with extraction/interpretation of variance structure para meters from a model built using gnls and varConstPower How about this: >

Hi, I'm trying to compare models, one of which has all parameters fixed using offsets. The log-likelihoods seem reasonble in all cases except the model in which there are no free parameters (model3 in the toy example below). Any help would be appreciated. Cheers, Jarrod x<-rnorm(100) y<-rnorm(100, 1+x) model1<-lm(y~x) logLik(model1) sum(dnorm(y, predict(model1),

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)

I have been using gnls with the weights argument (and varConstPower) to specify a variance structure for curve fits. In attempting to extract the parameters for the variance model I am seeing results I don't understand. When I simply display the model (or use "summary" on the model), I get what seem like reasonable values for both "power" and "const". When I

Dear R users, i'm using a custom function to fit ancova models to a dataset. The data are divided into 12 groups, with one dependent variable and one covariate. When plotting the data, i'd like to add separate regression lines for each group (so, 12 lines, each with their respective individual slopes). My 'model1' uses the group*covariate interaction term, and so the coefficients

Hello, I want to perform an lme on a database with this structure: ID Sequence Temperature Tumour Error 1 5 0 1 8.721872e-08 1 5 0 2 8.695348e-08 1 5 0 3 2.019604e-13 1 5 37 1

I am running an lme model with the main effects of four fixed variables (3 continuous and one categorical – see below) and one random variable. The data describe the densities of a mite species – awsm – in relation to four variables: adh31 (temperature related), apsm (another plant feeding mite) awpm (a predatory mite), and orien (sampling location within plant – north or south). I have read