similar to: Brier score for extended Cox model

Hello, I am using the val.prob function in the Design package. I understand how the Brier quadratic error score is calculated, but I do not know how the Brier score computed on the calibrated rather than raw predicted probabilities (B cal) is calculated. My question is: how are the calibrated probabilities calculated? Any explanation of this, or references to explanations of this, would be

Hello everyone, These are some questions about the 'pec' function in R. These questions deal with prediction error curves and their derivation. Prediction error curves are documented in, for example, "Efron-type measures of prediction error for survival analysis" by Gerds and Schumacher. I have detailed some syntax that I have used at the bottom of this email. The associated

System: R 2.3.1 on a Windows XP computer. I am validating several cancer prognostic models that have been published with a large independent dataset. Some of the models report a probability of survival at a specified timepoint, usually at 5 and 10 years. Others report only the linear predictor of the Cox model. I have used Harrell's c index for censored data (rcorr.cens) as a measure of

Hello I've decided to try and distill an earlier rather ill focused question to try and elicit a response. Any help is greatly appreciated. Why does mod.cox not work with sbrier whilst mod.km does? Can I make it work? > data(DLBCL) > DLBCL.surv<-Surv(DLBCL$time,DLBCL$cens) > > mod.km<-survfit(DLBCL.surv) > mod.cox<-survfit(coxph(DLBCL.surv~IPI, data=DLBCL)) >

Hi, I have probability estimates from two predictive models. I have these estimates and also a binary outcome for a validation data set not used in calibrating either model. I would like to calculate the Brier score for both models on this binary outcome and test the hypothesis that the Brier scores are equal from the two models. I have not been able to find an R function to do this, can

Hi, I am attempting to evaluate the prediction error of a coxph model that was built after feature selection with glmnet. In the preprocessing stage I used na.omit (dataset) to remove NAs. I reconstructed all my factor variables into binary variables with dummies (using model.matrix) I then used glmnet lasso to fit a cox model and select the best performing features. Then I fit a coxph model

Dear All, I got a warning message "X matrix deemed to be singular" in Cox model with a time dependent coefficient. In my analysis, the variable "SEX" is a categorical variable which violate the PH assumption in Cox. I first used the survSplit() function to break the data set into different time intervals, and then fit the model. The procedures can be described as follows:

Dear R users, I am doing Cox regression using coxph(Survival) or cph(Design). I have time varying effects (diagnosed with schoenfeld residuals Chi2 test and graph) so I first want to split time into 2 separate intervals : t<6months and t>=6months, to estimate one hazard ratio (hr) for each interval. I am analysing Overall survival according to 3 prognostic factors (age,deep,ldh).

This is a follow up to the message I posted 3 days ago about how to estimate mixed ordinal logit models. I hope you don't mind that I am just pasting in the code and comments from an R file for your feedback. Actual estimates are at the end of the post. ### Subject: mixed ordinal logit via "augmented" data setup. ### I've been interested in estimating an ordinal logit model

To Danardonos concern of splitting time for records with delayed entry: This can fairly easily be accomodated, by simply splitting time in small intervals of time since entry into the study, and then compute the value of the other timescales for each of these e.g.: current.age <- time.from.entry + age.at.entry but the cut on the other timescales will not be exactly where you may want them

Dear R-help Community, I'm currently struggling with some issues extending the proportional Cox model with time-dependent coefficients and could really need some help. Since I'm not experienced in adding code in an email in a nice way I add the link to my question and code: https://stats.stackexchange.com/questions/297052/time-dependend-coefficients-in-a-cox-model Essentially, I try

Dear All, since years I am struggling with Surv objects in data.frames. The following seems to have to do with it. See below the modified example from the help page of survSplit. The original works, as expected. If, however, a Surv object is added to the data.frame, each record gets doubled. Is there some solution other than avoiding Surv objects in data.frames? Thanks, Heinz

Im trying to write a controller spec, but when I run it I get an error like the one below. I found Bug6707 (http://rubyforge.org/pipermail/rspec-devel/2007-March/002147.html ), but I get the error no matter how I run the spec ( whether I use rake or not) Any ideas? I am using 0.8.2 The Error: C:/rails/InstantRails/ruby/lib/ruby/gems/1.8/gems/rspec-0.8.2/lib/spec/expectati ons/sugar.rb:14:in

Dear R-users, I use the survsplit function in the survival package to change my data into counting-process format and the transformed format is as follow: (a) start stop event DP age .... 0 5 0 1 20 5 10 0 1 20 10 25 1 1 20 looking at the above three entries that belong to the same person, if an event happen at

...something is still wrong. On the shared folders the commands "select" and "examine" work only the first time: -------%<-------------------------------------------------------- 3 select "shared/user1 at pec.inail.gov.it" * FLAGS (\Answered \Flagged \Deleted \Seen \Draft Junk) * OK [PERMANENTFLAGS (\Answered \Flagged \Deleted \Seen \Draft Junk \*)] Flags

I'm trying to convert a dataset from the time-independent analysis form > head(addicts) id clinic status survtime prison meth clinic01 1 1 1 1 428 0 50 1 2 2 1 1 275 1 55 1 3 3 1 1 262 0 55 1 into the "counting data format" necessary to perform extended Cox regression. I got

Hi, i have a problem with dovecot 2.0.13. I have one dovecot in Front-end that has only: passdb { driver = static args = proxy=y host=10.0.0.6 nopassword=y } In Back-end i have one dovecot that does auth and exports imap/pop3 ports. In dovecot's log of Front-end i see: /Apr 02 14:33:34 imap-login: Info: proxy(//user at example.com/ <mailto:amministrazionevendite at

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

Hi all, I have another question about the lrm function (from the rms library) that I cannot find the answer to. I get an error message when I try to fit a model, and I don't know what to make of it. Please forgive me for not having a toy example, but it appears the size and complexity of my data is somehow causing the error. The best I can do is show you what I type and what errors I get.

Hi, I am new to time-dependent Cox model to estimate time dependent hazard ratios. Let me use aml dataset from survival package: > aml3<-survSplit(aml2,cut=c(5,10,20),end="time",start="start", event="status",episode="i") If I want to esimate hazard ratio for each of the time intervals 0-5, 5-10, 10-20 and >=20, would the following calculate