plot_surv_quantiles: Plot Survival Time Quantiles as a Function of a Continuous Variable

Description

Using a previously fit time-to-event model, this function plots one or multiple survival time quantiles (such as the median survival time) as a function of a continuous variable of interest.

Usage

plot_surv_quantiles(time, status, variable, group=NULL,
                    data, model, na.action=options()$na.action,
                    p=0.5, horizon=NULL,
                    size=1, linetype="solid", alpha=1,
                    custom_colors=NULL, single_color=NULL,
                    xlab=variable, ylab="Survival Time Quantile",
                    title=NULL, subtitle=NULL,
                    legend.title=variable, legend.position="right",
                    gg_theme=ggplot2::theme_bw(),
                    facet_args=list(), ...)

Value

Returns a ggplot2 object.

Arguments

time: A single character string specifying the time-to-event variable. Needs to be a valid column name of a numeric variable in data.
status: A single character string specifying the status variable, indicating if a person has experienced an event or not. Needs to be a valid column name of a numeric or logical variable in data.
variable: A single character string specifying the continuous variable of interest, for which the survival curves should be estimated. This variable has to be contained in the data.frame that is supplied to the data argument.
group: An optional single character string specifying a factor variable in data. When used, the plot is created conditional on this factor variable, meaning that a facetted plot is produced with one facet for each level of the factor variable. See curve_cont for a detailed description of the estimation strategy. Set to NULL (default) to use no grouping variable.
data: A data.frame containing all required variables.
p: A numeric vector containing the survival time quantiles of interest. For example, if the user is interested in plotting only the median survival time p=0.5 should be used. When multiple values are supplied, one curve is drawn for each quantile.
model: A model describing the time-to-event process (such as an coxph model). Needs to include variable as an independent variable. It also has to have an associated predictRisk method. See ?predictRisk for more details.
na.action: How missing values should be handled. Can be one of: na.fail, na.omit, na.pass, na.exclude or a user-defined custom function. Also accepts strings of the function names. See ?na.action for more details. By default it uses the na.action which is set in the global options by the respective user.
horizon: A numeric vector containing a range of values of variable for which the survival curves should be calculated or NULL (default). If NULL, the horizon is constructed as a sequence from the lowest to the highest value observed in variable with 100 equally spaced steps.
size: A single number specifying how thick the lines should be drawn.
linetype: The linetype of the drawn lines. See documentation of ggplot2 for more details on allowed values.
alpha: The transparency level of the lines.
custom_colors: An optional character vector specifying the colors that should be used when multiple quantiles were supplied to the p argument. To set the whole plot or a single curve to one color only, use the single_color argument instead.
single_color: A single character string specifying the color of all drawn lines.
xlab: A character string used as the x-axis label of the plot.
ylab: A character string used as the y-axis label of the plot.
title: A character string used as the title of the plot.
subtitle: A character string used as the subtitle of the plot.
legend.title: A character string used as the legend title of the plot.
legend.position: Where to put the legend. See ?theme for more details.
gg_theme: A ggplot2 theme which is applied to the plot.
facet_args: A named list of arguments that are passed to the facet_wrap function call when creating a plot separated by groups. Ignored if group=NULL. Any argument except the facets argument of the facet_wrap function can be used. For example, if the user wants to allow free y-scales, this argument could be set to list(scales="free_y").
...: Further arguments passed to curve_cont.

Author

Robin Denz

Details

Survival Time Quantiles are a single value summarizing the entire survival curve. For example, the most prominently used survival time quantile is the median survival time, which can be interpreted as the time at which half of the people in the sample are expected to have experienced the event of interest. This plot shows one or more of these quantiles as a function of a continuous variable of interest.

To calculate the survival time quantiles, it first calls the curve_cont function to get estimates of the value-specific survival curves. Afterwards, it uses step function interpolation to read off the survival time quantile from the estimates.

Although this is a simple way to plot the effect of a continuous covariate on the survival, it can give a misleading visualization of the relationship in some situations. Plots that do not use summary statistics, such as the plot_surv_contour and plot_surv_area plots, may be preferable.

Examples

Run this code

library(contsurvplot)
library(riskRegression)
library(survival)
library(ggplot2)
library(splines)

# using data from the survival package
data(nafld, package="survival")

# take a random sample to keep example fast
set.seed(42)
nafld1 <- nafld1[sample(nrow(nafld1), 150), ]

# fit cox-model with age
model <- coxph(Surv(futime, status) ~ age, data=nafld1, x=TRUE)

# plot effect of age on the median survival time
plot_surv_quantiles(time="futime",
                    status="status",
                    variable="age",
                    data=nafld1,
                    model=model)

# plot multiple survival time quantiles
plot_surv_quantiles(time="futime",
                    status="status",
                    variable="age",
                    data=nafld1,
                    model=model,
                    p=c(0.1, 0.25, 0.5, 0.75, 0.9))

## showing non-linear effects

# fit cox-model with bmi modelled using B-Splines,
# adjusting for age and sex
model2 <- coxph(Surv(futime, status) ~ age + male + bs(bmi, df=3),
                data=nafld1, x=TRUE)

# plot effect of bmi on survival
plot_surv_quantiles(time="futime",
                    status="status",
                    variable="bmi",
                    data=nafld1,
                    model=model2,
                    p=c(0.1, 0.25, 0.5, 0.75, 0.9))

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