I am interested in making complex curves like these more interpretable. I guess it might be possible to improve things with some graphical tweaks and I welcome any suggestions for how to do so in R (I am using packages
survival). But I think what I really need to do to improve interpretability is reduce the number of lines shown on the plot by binning the continuous variable. I am asking the community for guidance about how and at what point should such binning occur?
Without knowing any better approach, my binning method would simply be to divide the variable into three categories:
with the median being computed after removing the 0 values. This method makes intuitive sense to me but I don't have any mathematical justification for it nor have I been able to find any examples of people doing something similar in survival analysis. If there's something problematic about it or if there's a better way, please let me know, but I am ultimately much less comfortable with the question of when to bin.
As for when the binning should occur, I am reluctant to bin the continuous variable before computing the Cox PH values because I think this could have a major impact the p-value and because my only rationale for binning this way is that I expect it will make my graphs easier to interpret. But if I bin the continuous variable after computing the Cox PH, I worry that that I'll be misrepresenting the data since the p-value will have been based on values which are masked by the binning.
Apologies if this is a common/simple/already-answered question. I am pretty new to data science and have not had much formal training (and none at all relating to survival analysis),