search for: grambsch

Dear R-Helpers: What other references are there on the capabilities of the survival5 package other than the help files and the chapter on survival analysis in every edition of Modern Applied Statistics with S? I'm thinking of something like "An Introduction to Survival Analysis in R" with worked examples that might complement or extend the chapter in MASS. Thanks, Spencer

To enhance my understanding, and that of my students, I have a question about cox.zph in the survival package. If I have correctly gleaned the high-level point from the 1994 Biometrika paper of Grambsch and Therneau, it looks to me like cox.zph provides a mechanism to test for a simple trend in plots of a function of time, g(t) versus the scaled schoenfeld residuals and it also provides some built-in ones and the capability to provide your own. It also appears to me that different forms look at d...

I'm trying to plot pspline'd explanatory variables from coxph() models as per Therneau and Grambsch (Modeling Survival Data: Extending the Cox Model). They have s-plus functions for this at: http://www.mayo.edu/hsr/people/therneau/book/sfunction/gamterms.s http://www.mayo.edu/hsr/people/therneau/book/sfunction/plotterm.s I'd like to make these plots in R, but they make use of non-R functio...

I will be giving a course in survival analysis using R (of course!) for people who know nothing about the subject (including R), but know basic statistics. I'm looking for a suitable course book. Therneau & Grambsch (2000) is an excellent book, but too much for this course. I need somthing more elementary. I have a vague memory saying that such books exist, but I cannot find any for the moment. Any suggestions are welcome. Thanks, G?ran -- G?ran Brostr?m tel: +46 90 786 5223 Departme...

Hi all, Has any body the exprience to iclude a nonparametric component into the survival analysis using R package? *Can someone recommend *me * some ** references? * Thanks a lot Ashta [[alternative HTML version deleted]]

...nu, flag = FALSE) > } > } > > so the recentering means the penalty is actually your penalty (2) -- not > surprising, as the code was written by Terry Therneau. Ok, I forgot to account for the recentering. However, correct me if I am wrong, but if Therneau and Grambsch (2000, Eq. (9.8)) are right, I think it should be: recenter <- mean(exp(coef)) instead of recenter <- log(mean(exp(coef))) > > The log density penalty doesn't give maximum likelihood (which you would > get by integrating out the frailties). It gives the...

I am trying to model data in which subjects are followed through time to determine if they fall, or do not fall. Some of the subjects fall once, some fall several times. Follow-up time varies from subject to subject. I know how to model time to the first fall (e.g. Cox Proportional Hazards, Kaplan-Meir analyses, etc.) but I am not sure how I can model the data if I include the data for those

...;- 2 group[id>=100 & id<150] <- 3 group[id>=150 & id<200] <- 4 group[id>=200 & id<250] <- 5 group[id>=250 & id<300] <- 6 group[id>=300] <- 7 I estimate the following model, using the Pbc data (with time-varying covariates) from Therneau and Grambsch's book: fitf <- coxph(Surv(start,stop,event==2) ~ age + log(bili) + log(protime) + log(albumin) + edema + frailty(group), na.action=na.exclude, data=Pbcseq) Then I obtain: > fitf[10] $frail [1] 0.06273372 0.16192093 0.10050877 0.3...

...hen use that fitted model to generate expected numbers of events for a new cohort; then, comparing the expected vs. the observed numbers of events would give us some idea of whether the new cohort differs from the reference one. >From my reading of the documentation and the text by Therneau and Grambsch, it seems that the function "survexp" is what I need. But using it I am not able to obtain expected numbers of events that match reasonably well the observed numbers *even for the same reference population.* So, I think I am misunderstanding something quite badly. Below is an example tha...

...can sometimes give seriously strange results, is to artificially remove ties from the data set by adding a random value to each subject's time. Terry T --- begin quote -- I didn't know precisely the specifities of each approximation method. I thus came back to section 3.3 of Therneau and Grambsch, Extending the Cox Model. I think I now see things more clearly. If I have understood correctly, both "discrete" option and "exact" functions assume "true" discrete event times in a model approximating the Cox model. Cox partial likelihood cannot be exactly maximized,...

...looking at 1 event (death), and repeated measurements (the time dependent covariate 'lqol') are frequently taken on a subject, so I assume that measurements on the same subject will be correlated. For this reason, I included the cluster(id) term in the model. However, on p70 Therneau and Grambsch, it states 'one concern that often arises is that observations on the same individual are correlated and thus would not be handled by standard methods. This is not actually an issue. .......................' so, does anyone recommend that I include the 'cluster(id)' term or does...

Dear all, I am looking for a good reference on "Survival analysis". I am looking for a booking containing both applications and Maths. Explaining different methods in survival analysis .... Many thanks Bernard --------------------------------- [[alternative HTML version deleted]]

[code]>require("survival") > coxph(Surv(futime,fustat)~age + strata(rx),ovarian) Call: coxph(formula = Surv(futime, fustat) ~ age + strata(rx), data = ovarian) coef exp(coef) se(coef) z p age 0.137 1.15 0.0474 2.9 0.0038 Likelihood ratio test=12.7 on 1 df, p=0.000368 n= 26, number of events= 12 > coxph(Surv(futime,fustat)~age, ovarian, subset=rx==1)

...estimated covariate effects between the groups. Therefore I specify a model with strata by covariate interactions. I would like to conduct a Wald test for the null hypothesis "no differences between any covariate effects in the 3 groups". This is similar to the example by Therneau & Grambsch in their book "Modeling survival data. Extending the Cox model", p. 47, except that I have interactions with many covariates that I would like to test jointly (and a frailty term). In S-plus there seems to be a function waldtest that does the job. Is there anything similar in R that I cou...

...o the John Fox 'Cox Proportional-Hazards Regression for Survival Data' http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-cox-regression.pdf and the corresponding script file at http://cran.r-project.org/doc/contrib/Fox-Companion/cox-regression.txt and also to Therneau and Grambsch. My problem is creating the dataset, possibly using the fold function (as described in Fox, p9) with more than one time-dependent covariate (which I successfully did with LAS). I have longitudinal measurements for each subject (with each date of assessment) as above with some missing data...

...3. If the other party is stubborn about SAS vs S, I have one example I like to argue with. The S survival code has an extensive test suite, including a set of small examples where I have worked out the correct results by hand. Many of these latter are documented in the appendix of the Therneau and Grambsch book. The SAS phreg procedure does not pass all the tests. (Cox model + Efron approx for ties + deviance residuals for one. The size of the error is numerically insignificant, a 1/n vs 1/(n-1) type of thing: but it leads to slightly different robust standard errors).

For a fitted Cox model, one can either produce the predicted survival curve for a particular "hypothetical" subject (survfit), or the predicted curve for a particular cohort of subjects (survexp). See chapter 10 of Therneau and Grambsch for a long discussion of the differences between these, and the various pitfalls. By default, survfit produces the curve for a hypothetical "average" subject whose covariate values are the respective means of the data set. I'm not very keen on this estimate --- what is sex=.453...

...impact on the fit, since only one group was at risk the deaths are guarranteed to come from that group. So the actual MLE for the hazard ratio is 1/0 = infinity, 100% death rate in group 1 vs. 0% in group 0, at all the time points where the two groups can be compared. Section 3.5 of Therneau and Grambsch, Extending the Cox Model, has a picture of the log-likelihood in such a case, which very quickly approaches an asymptote as beta goes to infinity. Both phreg and coxph iterate until the loglik "doesn't change anymore". The printed solution depends entirely on the convergence criteri...

Dear all, Is there a way to calculate the non-cumulative hazard (instantaneous hazard), which is the product of baseline hazard and exp{beta*covariate} ? I knew in survfit, we can get the estimator of cumulative baseline hazard, but how can we get the non-cumulative one? Thank you very much! Koshihaku -- View this message in context:

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]]