Plot a Heatmap of the Effect of a Continuous Covariate on a Time-To-Event Outcome

Using a previously fit time-to-event model, this function plots a heatmap with the continuous covariate on the y-axis and the time-to-event on the x-axis. The color is made in accordance with the corresponding survival probability or CIF at that point.

Usage

plot_surv_heatmap(time, status, variable, group=NULL,
                  data, model, cif=FALSE,
                  na.action=options()$na.action,
                  horizon=NULL, fixed_t=NULL, max_t=Inf,
                  start_color=NULL, end_color=NULL,
                  alpha=1, xlab="Time", ylab=variable,
                  title=NULL, subtitle=NULL,
                  legend.title="S(t)", legend.position="right",
                  gg_theme=ggplot2::theme_bw(),
                  facet_args=list(), panel_border=FALSE,
                  axis_dist=0, interpolate=TRUE,
                  contour_lines=FALSE, contour_color="white",
                  contour_size=0.3, contour_linetype="dashed",
                  ...)

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.
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.
cif: Whether to plot the cumulative incidence (CIF) instead of the survival probability. If multiple failure types are present, the survival probability cannot be estimated in an unbiased way. This function will always return CIF estimates in that case.
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 40 equally spaced steps.
fixed_t: A numeric vector containing points in time at which the survival probabilities should be calculated or NULL (default). If NULL, the survival probability is estimated at 100 equally spaced steps from 0 to the maximum observed event time.
max_t: A number indicating the latest survival time which is to be plotted.
start_color: The color used for the lowest value in horizon. This and the end_color argument can be used to specify custom continuous color scales used in the plot. For example, if a black and white plot is desired, the user can set start_color="white" and end_color="black". See ?scale_color_gradient for more information.
end_color: The color used for the highest value in horizon. See argument start_color.
alpha: The transparency level of the plot.
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").
panel_border: Whether to draw a border around the heatmap or not. Is set to FALSE by default to mimic standard heatmaps.
axis_dist: The distance of the axis ticks to the colored heatmap. Is set to 0 by default to mimic standard heatmaps.
interpolate: Whether to linearly interpolate the colors or not. Set to TRUE by default, which results in a smooth surface being plotted. Corresponds to the interpolate argument in the geom_raster function, which is used internally.
contour_lines: Whether to add some contour lines to the heatmap. To get a proper contour plot, use the plot_surv_contour function instead.
contour_color: The color of the contour lines. Defaults to "white". Ignored if contour_lines=FALSE.
contour_size: The size of the contour lines. Ignored if contour_lines=FALSE.
contour_linetype: The linetype of the contour lines. Defaults to "dashed". Ignored if contour_lines=FALSE.
...: Further arguments passed to curve_cont.

Details

Heatmaps are a great tool to visualize a three dimensional surface in a two-dimensional plot. A continuous color scale is used to represent the probability of interest. Although this is fine theoretically, it is often hard to read specific information off these plots. Contour lines can be added to the plot in order to make this easier by using contour_lines=TRUE in the function call.

In most cases, however, it is probably better to use a proper contour plot instead, which can be produced using the plot_surv_contour function. This is mostly a matter of taste, which is why both types of plots are included in this package. Another alternative is the to use the plot_surv_matrix function, which is basically a discretized version of a survival heatmap.

The main advantage of the heatmap and the contour plots is that they can visualize the effect of a continuous covariate regardless of how it was modeled. Non-linear relationships can be visualized just as well as linear ones. The major downside is, that the structure of the plot is not the same as that of a standard Kaplan-Meier plot. An alternative that is closer to the standard plot can be created using the plot_surv_area function.

Value

Returns a ggplot2 object.

Author

Robin Denz

Examples

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 survival using defaults
plot_surv_heatmap(time="futime",
                  status="status",
                  variable="age",
                  data=nafld1,
                  model=model)


# plot it only for 60 to 80 year old people
plot_surv_heatmap(time="futime",
                  status="status",
                  variable="age",
                  data=nafld1,
                  model=model,
                  horizon=seq(60, 80, 0.5))


## 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 using defaults
plot_surv_heatmap(time="futime",
                  status="status",
                  variable="bmi",
                  data=nafld1,
                  model=model2)


# plot effect of bmi on survival with contour lines
plot_surv_heatmap(time="futime",
                  status="status",
                  variable="bmi",
                  data=nafld1,
                  model=model2,
                  contour_lines=TRUE)