Plot Individual Survival Curves or CIFs for Specific Values of a Continuous Covariate

Using a previously fit time-to-event model, this function plots survival curves or CIFs that would have been observed if every individual in the dataset had been set to specific values of a continuous covariate.

Usage

plot_surv_lines(time, status, variable, group=NULL,
                data, model, cif=FALSE, conf_int=FALSE,
                conf_level=0.95, n_boot=300,
                na.action=options()$na.action,
                horizon=NULL, fixed_t=NULL, max_t=Inf,
                discrete=TRUE, custom_colors=NULL,
                start_color="blue", end_color="red",
                size=1, linetype="solid", alpha=1,
                xlab="Time", ylab="Survival Probability",
                title=NULL, subtitle=NULL,
                legend.title=variable, legend.position="right",
                gg_theme=ggplot2::theme_bw(),
                facet_args=list(), ci_alpha=0.4,
                kaplan_meier=FALSE, km_size=0.5,
                km_linetype="solid", km_alpha=1,
                km_color="black", km_ci=FALSE,
                km_ci_type="plain", km_ci_level=0.95,
                km_ci_alpha=0.4, ...)

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.
conf_int: Whether to plot point-wise bootstrap confidence intervals or not.
conf_level: A number specifying the confidence level of the bootstrap confidence intervals. Ignored if conf_int=FALSE.
n_boot: A single integer specifying how many bootstrap repetitions should be performed. Ignored if conf_int=FALSE.
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 12 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 every point in time at which an event occurred.
max_t: A number indicating the latest survival time which is to be plotted.
discrete: Whether to use a continuous color scale or a discrete one (default). If FALSE, the default ggplot2 colors are used.
custom_colors: An optional character vector of colors to use when discrete=FALSE. Ignored if discrete=TRUE, in which case the color gradient can be defined using the start_color and end_color arguments.
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.
size: A single number specifying the size of the drawn curves.
linetype: A single character string specifying the linetype of the curves.
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").
ci_alpha: A single number defining the transparency level of the confidence interval bands.
kaplan_meier: Whether to add a standard Kaplan-Meier estimator to the plot or not. If group is defined, the Kaplan-Meier estimator will be stratified by the grouping variable. If cif=TRUE was used, the cumulative incidence will be displayed instead of the survival curve.
km_size: The size of the Kaplan-Meier line. Ignored if kaplan_meier=FALSE.
km_linetype: The linetype of the Kaplan-Meier line. Ignored if kaplan_meier=FALSE.
km_alpha: The transparency level of the Kaplan-Meier line. Ignored if kaplan_meier=FALSE.
km_color: The color of the Kaplan-Meier line. Ignored if kaplan_meier=FALSE.
km_ci: Whether to draw a confidence interval around the Kaplan-Meier estimates. Ignored if kaplan_meier=FALSE.
km_ci_type: Which type of confidence interval to calculate for the Kaplan-Meier estimates. Corresponds to the conf.type argument in the survfit function. Ignored if kaplan_meier=FALSE or km_ci=FALSE.
km_ci_level: Which confidence level to use for the confidence interval of the Kaplan-Meier estimates. Ignored if kaplan_meier=FALSE or km_ci=FALSE.
km_ci_alpha: The transparency level of the confidence interval of the Kaplan-Meier estimates. Ignored if kaplan_meier=FALSE or km_ci=FALSE.
...: Further arguments passed to curve_cont.

Details

A simple plot of multiple covariate-specific survival curves. Internally, it uses the curve_cont function to calculate the survival curves.

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_lines(time="futime",
                status="status",
                variable="age",
                data=nafld1,
                model=model)


# plot it only for some specific user-defined values
plot_surv_lines(time="futime",
                status="status",
                variable="age",
                data=nafld1,
                model=model,
                horizon=c(40, 52, 63, 81))


## 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_lines(time="futime",
                status="status",
                variable="bmi",
                data=nafld1,
                model=model2,
                horizon=c(20, 30, 40))