plot_surv_3Dsurface {contsurvplot} | R Documentation |

## Plot the Survival Curve or CIF Dependent on a Continuous Variable as a 3D Surface

### Description

Using a previously fit time-to-event model, this function plots the survival curve as a function of a continuous variable using a 3D surface representation. This function can produce interactive plots using the plotly package, or a static image using the `persp`

function of the graphics package.

### Usage

```
plot_surv_3Dsurface(time, status, variable, data, model,
cif=FALSE, na.action=options()$na.action,
horizon=NULL, fixed_t=NULL, max_t=Inf,
interactive=FALSE,
xlab="Time", ylab="Survival Probability",
zlab=variable, ticktype="detailed",
theta=120, phi=20, col="green", shade=0.5,
...)
```

### Arguments

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

`data` |
A |

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

`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: |

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

`fixed_t` |
A numeric vector containing points in time at which the survival probabilities should be calculated or |

`max_t` |
A number indicating the latest survival time which is to be plotted. |

`interactive` |
Whether to draw the 3D surface as a static plot ( |

`xlab` |
A character string used as the x-axis label of the plot, representing the survival time. |

`ylab` |
A character string used as the y-axis label of the plot, representing the survival probability or CIF. |

`zlab` |
A character string used as the z-axis of the plot, representing the continuous covariate of interest. |

`ticktype` |
A single character: |

`theta` |
Angles defining the viewing direction. |

`phi` |
See argument |

`col` |
The color(s) of the surface facets. Transparent colours are ignored. This is recycled to the |

`shade` |
The shade at a surface facet is computed as |

`...` |
Further arguments passed to |

### Details

The survival curve or CIF dependent on a continuous variable can be viewed as a 3D surface. All other plots in this package try to visualize this surface by reducing it to two dimensions using color scales or summary statistics. This function on the other hand directly plots the 3D surface as such.

Although 3D plots are frowned upon by many scientists, it might be a good visualisation choice from time to time. Using the `interactive=TRUE`

option makes the plot interactive, which lessens some of the valid criticism of 3D graphics. Similar looking plots for the same purpose but using a likelihood-ratio approach have been proposed in Smith et al. (2019).

### Value

Returns a `plotly`

object if `interactive=TRUE`

is used and a matrix when `interactive=FALSE`

is used (still drawing the plot).

### Author(s)

Robin Denz

### References

Smith, A. M.; Christodouleas, J. P. & Hwang, W.-T. Understanding the Predictive Value of Continuous Markers for Censored Survival Data Using a Likelihood Ratio Approach BMC Medical Research Methodology, 2019, 19

### Examples

```
library(contsurvplot)
library(riskRegression)
library(survival)
library(splines)
library(ggplot2)
library(plotly)
# using data from the survival package
data(nafld, package="survival")
# take a random sample to keep example fast
set.seed(41)
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 for ages 50 to 80
plot_surv_3Dsurface(time="futime",
status="status",
variable="age",
data=nafld1,
model=model,
horizon=seq(50, 80, 0.5),
interactive=FALSE)
# plot effect of age on survival using an interactive plot for ages 50 to 80
plot_surv_3Dsurface(time="futime",
status="status",
variable="age",
data=nafld1,
model=model,
horizon=seq(50, 80, 0.5),
interactive=TRUE)
## showing non-linear effects
# fit cox-model with bmi modeled using B-Splines,
# adjusting for age
model2 <- coxph(Surv(futime, status) ~ age + bs(bmi, df=3),
data=nafld1, x=TRUE)
# plot effect of bmi on survival using normal plot
plot_surv_3Dsurface(time="futime",
status="status",
variable="bmi",
data=nafld1,
model=model2,
interactive=FALSE)
# plot effect of bmi on survival using interactive plot
plot_surv_3Dsurface(time="futime",
status="status",
variable="bmi",
data=nafld1,
model=model2,
interactive=TRUE)
```

*contsurvplot*version 0.2.1 Index]