similar to: Modeling time varying effects in with cph: how to ?

I am trying to build a model to aid a clinical decision. Certain patients have a blood marker measured at each visit - a rise of this may indicate recurrence of the cancer after treatment (endpoint is "clinical recurrence", censored). In a proportion (up to 30%), this rise is a false positive - hence I wish to correlate factors at the time of the rising test to clinical recurrence,

Hi, I have fitted a cox model with time-varying covariates (counting process style) using the cph function of the Design package. Now I want to know the survival probabilities at each time point given the history of a single individual. I know the survest function, but I am not sure how to interpretet its output when using time-varying covariates. Does it just give the probabilities as if it

Hi, I am trying to run a conditional logistic model on a nested case-control study using cph() and then estimate survival based on the model. The data came from Prof Bryan Langholz website where he also has the SAS code to this, so I am trying to replicate the SAS results. The data attached. Basically, the variables are: rstime: risk set age rsentry: fake entry time, just before rstime setno:

Sorry everybody, temporary dyslexia. Sent from my BlackBerry wireless smartphone

You wrote: "It may be best to either write to the package maintainer (me, as you did) or post to the group but not both." This is just a note that I disagree wrt my own packages: I go on vacation or trips, or have other projects so won't always answer Other folks on the list often have good ideas that I'd miss My preferred standard is "ask the list, with a copy

Hi all! I'm trying to plot validate.cph. I have a problem because I'm collating several plots but I can't reduce the size of the plots otherwise the labels below the plot overlap.  If I remove the footnotes, I can add it in the main text.  How can I remove the footnotes i.e. stuff like black:observed gray:optimism ... Salvo [[alternative HTML version deleted]]

Hi, List, I am using function 'cph' in package 'Design'. I have run into this error message but could not find documentation after looking for a long time. Could someone help me out? What kind of problem it is in my data set and how to fix it? Thanks a lot! Auston Error in fitter(X, Y, strata = Strata, offset = offset, weights = weights, :NA/NaN/Inf in foreign function

Hi all, I am trying to get bootstrap resampled estimates of covariates in a Cox model using cph (Design library). Using the following I get the error: > ddist2.abr <- datadist(data2.abr) > options(datadist='ddist2.abr') > cph1.abr <- cph(Surv(strt3.abr,loc3.abr)~cov.a.abr+cov.b.abr, data=data2.abr, x=T, y=T) > boot.cph1 <- bootcov(cph1.abr, B=100, coef.reps=TRUE,

Dear all, I used cph() function from Frank harrell's Design package to create a survival model, then used functions 'Function' and 'sascode' to generate prediction equation based on the saved survival model. But it failed. I included a stratified variable in the model. If I removed the stratification, they were working well. Does that mean that function 'Function'

Hello, I am a beginner in R and statistics, so my question may be trivial. Sorry in advance. I performed a Cox proportion hazard regression with 2 categorical variables with cph{design}. Then an anova on the results. the output is > anova(cph(surv(survival, censor) ~ plant + leaf.age + plant*leaf.age, > Mpnymph) Wald Statistics Response: Surv(survival, censored)

Hi all (and especially Frank), I'm trying to use x=T, y=T in order to run a validated stepwise cox regression in rsm, having multiply imputed using mice. I'm coding model.max<-fit.mult.impute(baseform,cph,miced2,dated.sexrisk2,x=T,y=T) baseform is baseform<-Surv(si.age,si=="Yes")~ peer.press + copy.press + excited + worried + intimate.friend + am.pill.times +

Dear all: I have used the cph function in the rms package. log10 was used to transform the variables, as follows: fit<-cph(pfsurv~log10(x1)+log10(x2),x=T,y=T,surv=T) after I run the nomogram function. I found "variable limits and transformations are such that an infinite axis value has resulted." How to add variable limits in the nomogram function? Thanks a lot *Yao Zhu*

Hi, the calibrate.cph() function in rms package generate calibration curve for Cox model on the same dataset where the model was derived using bootstrapping or cross-validation. If I have the model built on dataset 1, and now I want to produce a calibration curve for this model on an independent dataset 2, how can I do that? Thanks John [[alternative HTML version deleted]]

Hi! How can I exclude the legends from calibration plots  generated by calibrate.cph regards, Salvo [[alternative HTML version deleted]]

Hello,   does anyone have an example on how to use restricted cubic splines function rcs within survfit.cph, if cph (Cox Proportional Hazard Regression) was done with restricted cubic splines (which I made to work)? Thank you. > [[alternative HTML version deleted]]

Dear R users, My apologies for the simple question, as I'm starting to learn the concepts behind the Cox PH model. I was just experimenting with the survival and rms packages for this. I'm simply trying to obtain the expected survival time (as opposed to the probability of survival at a given time t). I can't seem to find an option from the "type" argument in the predict

Hello, I am constructing a nomogram using cph and nomogram commands in Dr. Harrell's Design/RMS package. The HR that I obtain for dichotomous and categorical variables are identical to those that I obtain using STATA stcox. However, the inter-quartile HR I obtain for continuous variables is obviously different, since STATA gives me HR for each unit (year, centimeter, etc) like coxph would

Hello, I am using Dr. Harrell's design package to make a nomogram. I was able to make a beautiful one. However, I want to change 5-year survival probability to 5-year failure probability. I couldn?t get hazard rate from Hazard(f1) because I used cph for the model. Here is my code: f1 <- cph(Surv(retime,dfs) ~ age+her2+t_stage+n_stage+er+grade+cytcyt+Cyt_PCDK2 , data=data11, surv=T,

Hi, in Design package, a plot of survival probability vs. a covariate can be generated by survplot() on a cph object using the folliowing code: n <- 1000 set.seed(731) age <- 50 + 12*rnorm(n) label(age) <- "Age" sex <- factor(sample(c('male','female'), n, TRUE)) cens <- 15*runif(n) h <- .02*exp(.04*(age-50)+.8*(sex=='Female')) dt <-

While sm4.6ll<-fit.mult.impute(Surv(agesi, si)~partner+ in.love+ pubty+ FPA+ strat(gender),fitter = cph, xtrans = dated.sexrisk2.i, data = dated.sexrisk2, x=T,y=T,surv=T, time.inc=16) runs perfectly using Hmisc, Design and mice under R11 run via Sciviews-K, with library(Design) library(mice) ds2d<-datadist(dated.sexrisk2) options(datadist="ds2d")