Please include example data in the future.  Perhaps the following is useful.

(1) Your model is redundant.  The "*" produces both main effects and the 
interaction.  So I removed the main effects from your call
(2) For my simulated data, the df=0 option chose a model that resulted in a 
singular fit.  I selected a smoother spline (df=2).
(3) the two plots at the end show (1) the risk (exp(linear predictor)) for 
combinations of "CONTINUOUS" and "DICHOTOMOUS" and (2) a ratio (risk for A vs 
risk for B), which I think is what you wanted.

Chris

library(survival)
set.seed(20140530)
nn <- 1000
datanew2 <- data.frame(my.surv = Surv(rexp(nn)), 
DICHOTOMOUS=factor(rep(c("A","B"), nn/2)), CONTINUOUS=rnorm(nn))

#surv.fit <- coxph(my.surv ~ pspline(CONTINUOUS, df=0) + factor(DICHOTOMOUS) + 
pspline(CONTINUOUS, df=0) * factor(DICHOTOMOUS), data=datanew2)
#surv.fit <- coxph(my.surv ~ pspline(CONTINUOUS, df=0) * factor(DICHOTOMOUS), 
data=datanew2)
surv.fit <- coxph(my.surv ~ pspline(CONTINUOUS, df=2) * factor(DICHOTOMOUS), 
data=datanew2)
surv.fit

xseq <- seq(-3, 3, length=100)
predictions <- matrix(predict(surv.fit, newdata=expand.grid(CONTINUOUS=xseq, 
DICHOTOMOUS=factor(c("A","B"))), type="risk"), ncol=2)
matplot(predictions, type="l")
plot(xseq, predictions[,1]/predictions[,2], type="l", ylab="Hazard Ratio of 
Event (A vs B)", xlab="CONTINUOUS")


-----Original Message-----
From: Lynn Dunsire [mailto:l...@contrastconsultancy.com] 
Sent: Thursday, May 29, 2014 6:03 AM
To: r-help@r-project.org
Subject: [R] Smoothed HR for interaction term in coxph model

Hello R-help members,

 

I have a dataset with 2 treatments and want to assess the effect of a
continous covariate on the Hazard ratio between treatment A and B.  I want a
smoothed interaction term which I have modelled below with the following
code:

 

surv.fit <- coxph(my.surv ~ pspline(CONTINUOUS, df=0) + factor(DICHOTOMOUS)
+  pspline(CONTINUOUS, df=0)*factor(DICHOTOMOUS), data = datanew2)

 

and consequently I would like to obtain a smoothed plot of the hazard ratio
between treatment A and B on the y-axis with the continuous covariate on the
x-axis.  As termplot ignores interaction terms, I was wondering if anyone
has seen anything like this before and can advise on the best way to do it.

 

Many thanks in advance for any help that you can offer,

 

Kind regards,

 

Lynn 


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