Enhancing KM plot data depth and functionality

[mhaist94]

Description of feature

Hi all,

thanks for all the previous help on the ehrapy features - you´re a great team! I have a couple of ideas that might help to enhance the visibility of ehrapy particularly for clinicians / clinician scientists (which could have a big impact on the progress in the field given that most clinicians still use old stat tech like spss or sas). To keep things in order I will open up a new issue for each of those ideas (stop me if this might not be useful!). One of those (which is probably an easy fix, but with making a big difference) is enhancing the data density within the KM plots (in order for clinicians to use ehrapy there´s a couple of features that have to be shown in such a KM plot) by adding the following:

• No at risk table
• Censors
• results of log-rank test as p-value plotted within the KM figure
• median survival highlighted

As I grew up mostly working with R I unfortunately dont have a python env at hand that implements all these features but can provide you the corresponding R-package (survminer, see https://rpkgs.datanovia.com/survminer/ or a general example in Figure 2A of this publication: https://jitc.bmj.com/content/11/9/e007630)

Let me know if this is somehow helpful or if you have any questions. Thanks all!

[Zethson]

These ideas are great! Thank you very much, please keep them coming.

[eroell]

This here could be helpful @aGuyLearning : https://www.nature.com/articles/s41586-024-08167-5