survminer (version 0.4.0)

ggcoxadjustedcurves: Adjusted Survival Curves for Cox Proportional Hazards Model

Description

This function plots adjusted survival curves for coxph model. The idea behind this function is described in https://cran.r-project.org/web/packages/survival/vignettes/adjcurve.pdf. For every observation in the dataset a prediction for survival curve is made. Then the predictions are averaged with respect to a selected variable.

Usage

ggcoxadjustedcurves(fit, variable = NULL, individual.curves = FALSE,
  data = NULL, fun = NULL, palette = "hue", curve.size = 2,
  curve.alpha = 0.2, ylab = "Survival rate", ggtheme = theme_survminer(),
  ...)

Arguments

fit

an object of class coxph.object - created with coxph function.

variable

a variable (vector) with values corresponding to groups to be plotted

individual.curves

if TRUE then all individual predicted survival curves will be plotted

data

a dataset for predictions. If not supplied then data will be extracted from `fit` object.

fun

an arbitrary function defining a transformation of the survival curve. Often used transformations can be specified with a character argument: "event" plots cumulative events (f(y) = 1-y), "cumhaz" plots the cumulative hazard function (f(y) = -log(y)), and "pct" for survival probability in percentage.

palette

the color palette to be used. Allowed values include "hue" for the default hue color scale; "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"). See details section for more information. Can be also a numeric vector of length(groups); in this case a basic color palette is created using the function palette.

curve.size, curve.alpha

size and alpha for individual survival curves

ylab

y axis label.

ggtheme

function, ggplot2 theme name. Allowed values include ggplot2 official themes: see theme.

...

further arguments passed to the function ggpar for customizing the plot.

Value

Returns an object of class gg.

Examples

# NOT RUN {
library(survival)
fit2 <- coxph( Surv(stop, event) ~ rx + size, data = bladder )
ggcoxadjustedcurves(fit2, data = bladder)
ggcoxadjustedcurves(fit2, individual.curves = TRUE, data = bladder, curve.alpha=0.01)
ggcoxadjustedcurves(fit2, data = bladder, variable= bladder[,"rx"])
ggcoxadjustedcurves(fit2, data = bladder, variable= bladder[,"rx"],
   individual.curves=TRUE, curve.alpha=0.01)

# }