plot_surv_rmst {contsurvplot} | R Documentation |

## Plot the Effect of a Continuous Variable on the Restricted Mean Survival Time

### Description

Using a previously fit time-to-event model, this function plots the restricted mean survival time (RMST) as a function of a continuous variable.

### Usage

```
plot_surv_rmst(time, status, variable, group=NULL,
data, model, na.action=options()$na.action,
tau, horizon=NULL, custom_colors=NULL,
size=1, linetype="solid", alpha=1, color="black",
xlab=variable, ylab="Restricted Mean Survival Time",
title=NULL, subtitle=NULL,
legend.title=variable, legend.position="right",
gg_theme=ggplot2::theme_bw(),
facet_args=list(), ...)
```

### Arguments

`time` |
A single character string specifying the time-to-event variable. Needs to be a valid column name of a variable in |

`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 variable in |

`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 |

`group` |
An optional single character string specifying a factor variable in |

`data` |
A |

`model` |
A model describing the time-to-event process (such as an |

`na.action` |
How missing values should be handled. Can be one of: |

`tau` |
The point in time to which the RMST should be calculated. Can be a vector of numbers. If multiple values are supplied, one curve is drawn for each of them. |

`horizon` |
A numeric vector containing a range of values of |

`custom_colors` |
An optional character vector of colors to use when there are multiple values in |

`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. |

`color` |
The color of the curve if |

`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 |

`gg_theme` |
A ggplot2 theme which is applied to the plot. |

`facet_args` |
A named list of arguments that are passed to the |

`...` |
Further arguments passed to |

### Details

Similar to the `plot_surv_at_t`

and `plot_surv_quantiles`

plots, this function produces a plot of a survival curve summary statistic as a function of a continuous variable. The summary statistic in question is the restricted mean survival time (RMST), which is the area under a survival curve up to a specific point in time `tau`

. It can be interpreted as the mean survival time of the population up to `tau`

.

First, a range of value-specific survival curves are estimated. The RMST is then calculated for each one using step function interpolation. This only works for survival curves, not for CIFs. If the area under the CIF should be used instead, the `plot_surv_rmtl`

function can be used.

An advantage of this method over the `plot_surv_at_t`

and `plot_surv_quantiles`

function is, that it does take the whole survival curve into account, kind of. Although it is the area under it, it is only calculated up to `tau`

which makes the output dependent on the choice of `tau`

. This, again, can result in deceiving plots in some cases. Plots visualizing the entire survival curves such as `plot_surv_contour`

and `plot_surv_area`

might be preferable.

### Value

Returns a `ggplot2`

object.

### Author(s)

Robin Denz

### References

Eng, K. H.; Schiller, E. & Morrell, K. On Representing the Prognostic Value of Continuous Gene Expression Biomarkers with the Restricted Mean Survival Curve. In: Oncotarget, 2015, 6, 36308-36318

Robin Denz, Nina Timmesfeld (2023). "Visualizing the (Causal) Effect of a Continuous Variable on a Time-To-Event Outcome". In: Epidemiology 34.5

### 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 the RMST for ages 50 to 80
plot_surv_rmst(time="futime",
status="status",
variable="age",
data=nafld1,
model=model,
horizon=seq(50, 80, 1),
tau=2500)
# plot RMST for multiple tau values for ages 50 to 80
plot_surv_rmst(time="futime",
status="status",
variable="age",
data=nafld1,
model=model,
horizon=seq(50, 80, 1),
tau=c(2000, 3000, 5000))
## showing non-linear effects
# fit cox-model with bmi modeled 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_rmst(time="futime",
status="status",
variable="bmi",
data=nafld1,
model=model2,
tau=c(2000, 3000, 5000))
```

*contsurvplot*version 0.2.1 Index]