similar to: Cox proportional hazard model and coefficients

Hello Again R Folks: I?m trying to clean up some code. Suppose I have an object like this: > str(test) List of 2 $ G:List of 2 ..$ cls:'data.frame': 101 obs. of 2 variables: .. ..$ V1: num [1:101] -0.0019 -0.0019 -0.00189 -0.00188 -0.00186 ... .. ..$ V2: num [1:101] 0.000206 0.000247 0.000288 0.000329 0.000371 ... ..$ rob:'data.frame': 101 obs. of 2

Thomas suggested posting these here so they don''t get lost. Making Scriptaculous Snappy: *) Drag/Drop - make release cost proportional to remaining distance For drag/drop - change the ''release'' animation time to be proportional to the remaining distance the dropped item has to travel. If a user drags the item almost all the way to the final location, the release time

I am trying to incorporate random effects (random intercept is good enough) to a proportional odds logistic regression model for ordinal outcomes. Could lme4 do this? I'd appreciate any input. Hakan Demirtas

To the list: What R commands will perform the score test on an ordered multinomial logit model to evaluate the proportional odds assumption? Many thanks. Mtichell Wachtel [[alternative HTML version deleted]]

Hi I have been asked to perform a multivariate survival analysis with competing risks. We used to use the Cox Regression from SPSS. To perform this new analysis I'm using the comp.risk function from the timereg package. I have managed to use it and get the final model and the correct P values, but I cannot find anything like the Exp(B) (the Hazard Ratio) in the function's result. Anyone

Dear community, I am currently attempting to perform a (L1) penalized ordinal logistic regression with proportional odds. For the moment I only found R packages allowing to perform forward or backward continuation ratio model with several penalizations. Does anyone have a clue of what R package I could use ? I am not even quite sure that penalized logistic regression with proportional odds has

Dear Dr. Fieberg, you used a regression tree approach to explore ordinal data set in addition to the proportinal odds model. I find this very interesting. I would like to know, how good the results of the regression tree approach turned out in comparison to the proportional odds model. Since people very often treat ordinal data as continuous, I would like to know how successfull this strategy

Hi useRs, Many studies of the link between red meat and colorectal cancer use Cox proportional hazards with (among other things) a gender covariate. If it is true that men eat more red meat, drink more alcohol and smoke more than women, and if it is also true that alcohol and tobacco are known risk factors then why does it make sense to "adjust" for gender? I would think that in this

Hello everyone, I am a new user of R. Does anybody know how hazard ratios are extracted for each factor level in a stratified Cox proportional hazard regression model? I have a cancer data set where the variable ?differentiation? is a factor with three levels: poor, intermediate and good. I would like to extract the hazard ratio for each grade level and relate it to another prognostic factor.

Is there an R function that I can use to calculate the p-value from the Z statistics computed for the relationship between chance and observed proportions in predictions. More sprcifically I am refering to proportional chance criteria (Cpro). Details are in Huberty's book on Applied discriminant analysis, unfortunately our library has misplaced the book. I got some details from the

Dear R-users, I am looking for a function designed to handle parametric proportional hazard models with a piecewise constant baseline hazard (i.e. dummies for annual intervals) and time-dependent covariates since I'm especially interested about the effect of those covariates on the baseline hazard. I tried survreg() but this doesn't seem to work since my data looks like this id/ start/

Hello dear R-help mailing list, I wish to perform a non-parametric repeated measures anova. If what I read online is true, this could be achieved using a mixed Ordinal Regression model (a.k.a: Proportional Odds Model). I found two packages that seems relevant, but couldn't find any vignette on the subject: http://cran.r-project.org/web/packages/repolr/

For the following model, library(vcd) arth.polr <- polr(Improved ~ Sex + Treatment + Age, data=Arthritis) summary(arth.polr) where Improved is an ordered, 3-level response I'm looking for a *simple* way to test the validity of the proportional odds assumption, typically done via a score test for equal slopes/effects over the predictors. I do find a po.test= option in the repolr package

I am trying to run a Cox regression (coxph) to predict survival after surgery and xray therapy. The model violates the proportionality assumption (tested using time-dependent covariates). I have tried to use strata to solve the problem but the stratified model still violates the assumption. Does the violation of proportionality preclude use of a Kaplan-Meier analysis? Is there any other possible

Hello, I need to implement variable selection procedures (such as stepwise and backward selection) in Cox proportional hazards model, but can't seem to find an R or S-plus command for these procedures. I am aware that these can be done in SAS. I would appreciate help from anyone who knows how to implement these procedures in Cox models using S-plus or (preferably) R. Thanks! Regards,

Dear All, I am contemplating centering the covariates in my Cox model to reduce multicollinearity between the predictors and the interaction term and to render a more meaningful interpretation of the regression coefficient. Suppose I have two indicator variables, x1 and x2 which represent age categories (x1 is patients less than 16 while x2 is for patients older than 65). If I use the following

Hi. I used coxph(surv(start,end,event)~~event,data) to deal with interval censor data. Does anyone know similar samples using coxph(surv(start,end,event)~~event,data)? If you knows, can you tell me? I'll really appreciate it. Thank you very much R learner.

Hi Since the 'equal slope' assumption doesn't hold in my data I cannot use a proportional odds model ('Design' library, together with 'Hmisc'). I would like to try therefore a partial proportional odds model Please, could anybody tell me, where to find the code and how to specify such a model ..or any potential alternatives many thanks for your kind help christoph

Greetings -- I would like to estimate a proportional hazard model with a weibull or lognormal baseline. I have looked at both the coxph() and survreg() functions and neither appear (to me ) to do it. Am I missing something in the docs or is there another terrific package out there that will do this. Many Thanks. Carl Mason

Dear List, Is the test for proportional hazards valid when the model contains a cluster variable? The output looks strange with the cluster variable. My intervals are based on calendar time and the clustering variable is related to the season the event occurs in. model1<-coxph(Surv(Start,Stop,Event)~LagAOO+I(LagAOO^2)+ FirstSeen+TSLE+strata(CumPOO.rc)+cluster(quarter),data=data8, x=T)