contsurvplot-package {contsurvplot} | R Documentation |

## Visualize the Effect of a Continuous Variable on a Time-To-Event Outcome

### Description

*What is this package about?*

This package provides different plotting routines to visualize the (causal) effect of a continuous variable on a time-to-event outcome using a previously fit model and g-computation. Unlike simpler alternatives, such as plotting survival curves for some categories, these plots always correspond to the results obtained by the time-to-event model and give an accurate depiction of the causal effect, if all assumptions are met.

*What features are included in this package?*

The package includes 11 different plot functions, most based on the ggplot2 package. Those 11 plotting routines include *value specific survival curves*, *landmark survival probability plots*, *survival time quantile plots*, *survival probability heatmaps*, and *survival area plots*, among others. A description and comparison of these plots can be found in the article associated with this R-package (Denz & Timmesfeld 2022).

*What does a typical workflow using this package look like?*

All the user has to do is fit a time-to-event model, such as the cox-model, including the continuous variable of interest (and possibly confounders) and plug it into one of the plot functions included in this package. Many different kind of models are supported. See `curve_cont`

for more details.

*What type of plot should I use?*

There is no general answer to this question, but we would usually suggest using a plot method that is able to visualize the causal survival probability both as a function of time and as a function of the continuous variable. The `plot_surv_area`

, `plot_surv_heatmap`

and `plot_surv_contour`

functions do just that. More discussion about this topic can be found in the vignette and the associated paper.

*What is the difference between displaying causal effects and associations?*

The plots generated by this package offer different ways to depict the association between a continuous variable and a time-to-event outcome. Under certain causal identifiability assumptions, which are described in detail in our article on this topic (see references), this association can be endowed with a causal interpretation. Under these assumptions, the estimates can be interpreted as the survival probability that would have been observed if all individuals in the target population had received a specific level of the continuous variable. If these assumptions are not met, this interpretation is invalid.

*Where can I get more information?*

The documentation pages contain a lot of information, relevant examples and literature references. Additional examples can be found in the vignette of this package, which can be accessed using `vignette(topic="introduction", package="contsurvplot")`

. We also published a preprint of the article about this package on arXiv (see references), which contains an in-depth discussion about the plots and how to interpret them.

*I want to suggest a new feature / I want to report a bug. Where can I do this?*

Bug reports, suggestions and feature requests are highly welcome. Please file an issue on the official github page (<https://github.com/RobinDenz1/contsurvplot>) or contact the author directly using the supplied e-mail address.

### Author(s)

Robin Denz (robin.denz@rub.de)

### References

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

James Robins. A New Approach to Causal Inference in Mortality Studies with a Sustained Exposure Period: Application to Control of the Healthy Worker Survivor Effect. Mathematical Modelling (1986) 7, pages 1393-1512.

*contsurvplot*version 0.2.1 Index]