How to use termplot function with fixed predictive variable values? - r

Question

Let´s assume I want to draw a plot similar to here here using R, i.e. hazard ratio on y axis and some predictor variable on x axis based on a Cox model with spline term. The only exception is that I want to set my own x axis points; termplot seems to pick all the unique values from the data set but I want to use some sort of grid. This is because I am doing multiple imputation which induces different unique values in every round. Otherwise I can do combined inference quite easily but it would be a lot easier to make predictions for the same predictor values every imputation round.
So, I need to find a way to use termplot function so that I can fix predictor values or to find a workaround. I tried to use predict function but its newdata argument requires values for all other (adjusting) variables too, which inflates standard errors. This is a problem because I am also plotting confidence intervals. I think I could do this manually without any functions except that spline terms are out of my reach in this sense.
Here is an illustrative example.
library(survival)
data(diabetic)
diabetic<-diabetic[diabetic$eye=="right",]
# Model with spline term and two adjusting variables
mod<-coxph(Surv(time,status)~+pspline(age,df=3)+risk+laser,data=diabetic)
summary(mod)
# Let's pretend this is the grid
# These are in the data but it's easier for comparison in this example
ages<-20:25
# Right SEs but what if I want to use different age values?
termplot(mod,term=1,se=TRUE,plot=F)$age[20:25,]
# This does something else
termplot(mod,data=data.frame(age=ages),term=1,se=TRUE,plot=F)$age
# This produces an error
predict(mod,newdata=data.frame(age=ages),se.fit=T)
# This inflates variance
# May actually work with models without categorical variables: what to do with them?
# Actual predictions are different but all that matters is the difference between ages
# and they are in line
predict(mod,newdata=data.frame(age=ages,risk=mean(diabetic$risk),laser="xenon"),se.fit=T)
Please let me know if didn't exlain my problem sufficiently. I tried to keep it as simple as possible.

Answer 1

In the end, this how I worked it out. First, I made the predictions and SEs using termplot function and then I used linear interpolation to get approximately right predictions and SEs for my custom grid.
ptemp<-termplot(mod,term=1,se=TRUE,plot=F)
ptemp<-data.frame(ptemp[1]) # Picking up age and corresponding estimates and SEs
x<-ptemp[,1]; y<-ptemp[,2]; se<-ptemp[,3]
f<-approxfun(x,y) # Linear interpolation function
x2<-seq(from=10,to=50,by=0.5) # You can make a finer grid if you want
y2<-f(x2) # Interpolation itself
f_se<-approxfun(x,se) # Same for SEs
se2<-f_se(x2)
dat<-data.frame(x2,y2,se2) # The wanted result