Predicted Survival Curves using Corrected Group Prognosis Method - r

Question

How can I plot predicted survival curves of a continuous covariate (let's say 20th and 80th percentile of the value) using the corrected group prognosis method as implemented in R by Therneau
For example,
library(survival)
library(survminer)
fit <- coxph( Surv(stop, event) ~ size + strata(rx), data = bladder )
ggadjustedcurves(fit, data=bladder, method = "conditional", strata=rx)
Now, this is useful because I am given two survival curves that are stratified by rx (either 0 or 1) and the conditional method is being acted upon the bladder data set. However, let's say I would like to use the marginal method but not stratify and instead plot my continuous covariate at 20th and 80th value but also re-balance the subpopulation. Would like any step in the right direction.
To re-state, I have a Cox model with continuous predictors. I would like to build a Cox model but not stratify on rx but have this in the model. Then, I want to pass the created Cox object into ggadjustedcurves() function with uses "subpopulation re-balancing" when given a reference data set. And then, instead of showing two survival curves stratified on a categorical variable, I want to plot two representative survival curves at the 20th and 80th percentile.
EDIT
My first attempt
fit2 <- coxph( Surv(stop, event) ~ size + rx, data = bladder ) #remove strata
fit2
# CGP
pred<- data.frame("rx" = 1, "size" = 3.2)
ggadjustedcurves(fit2, data = pred , method = "conditional", reference = bladder)
Is this what I think it is? Conditional re-balancing has been applied to the reference data set and then the predicted curves are generated for an individual with rx=1 and size of 3.2.

Answer 1

It is difficult to understand what you are truly looking for, but I think I have a rough idea. I think you want to plot the survival curve that would have been observed if every person in your sample had received a specific value for the continuous covariate. If there is no confounding, you can simply use a Cox model that includes only the continuous covariate and use the predict() function for a range of points in time and plot the results. If you need to adjust for confounding, you can include the confounders in the Cox model and use g-computation to obtain the desired probabilities. I describe this in a recent preprint: https://arxiv.org/pdf/2208.04644.pdf
This can be done in R using the contsurvplot package (also developed by me). First, install the package using:
devtools::install_github("RobinDenz1/contsurvplot")
Afterwards, fit your Cox model, but use x=TRUE in the coxph call:
library(survival)
library(contsurvplot)
library(riskRegression)
library(ggplot2)
fit2 <- coxph(Surv(stop, event) ~ size + rx, data=bladder, x=TRUE)
You can now call the plot_surv_lines function to obtain the causal survival curves for specific values of size, given the model. Using the horizon argument you can tell the function for which values you want to plot the survival curves. I choose the 20% and 80% quantile of size as you described:
plot_surv_lines(time="stop",
status="event",
variable="size",
data=bladder,
model=fit2,
horizon=quantile(bladder$size, probs=c(0.2, 0.8)))
The package contains a lot more plotting routines to visualize the causal effect of a continuous variable on a time-to-event outcome that might be more suitable for what you actually want.