<p>As gdrouard suggested, categorizing might not be your best option. Using a suitable time-to-event regression model (such as the Cox proportional hazards model) is usually preferable when analyzing continuous variables. The reason for this is that you are basically throwing away information if you artificially categorize it. This may also lead to bias in some scenarios.</p>
<p>If you want to visualize the effect of the continuous covariate on the time-to-event outcome afterwards, you may be interested in the <code>contsurvplot</code> R-package (<a href="https://github.com/RobinDenz1/contsurvplot" rel="nofollow noreferrer">https://github.com/RobinDenz1/contsurvplot</a>) I created. You can simply plug your regression model into one of the included plot functions and get a nice plot of the effect. More information can be found in the associated preprint: <a href="https://arxiv.org/pdf/2208.04644.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/2208.04644.pdf</a></p>
How do I find the best categorization for survival analysis?
<p>I have a question concerning survival analysis. However, I have the following data (just an excerpt):</p>
<p><a href="https://i.stack.imgur.com/tscd9.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/tscd9.png" alt="enter image description here" /></a></p>
<p>Now I am trying to do Survival Analysis with Python lifelines package. For example I want to find out if T-cells influence the Overall Survival (OS). But as far as I know, I need to categorizie the numer of T cells in different categories, like e.g. High T-Cell and Low T-Cell... Is that right? But how do I find out the best fitting Cut-Out?
My plan is to show, that Tumor with High T-Cells have a better survival than low T-Cells. But how could I find the best cut-off-value to discriminate between High and Low T-Cell out of the data I have here.</p>
<p>Does anyone has an idea? A friend of mine said something about &quot;ROC&quot;-Analysis but I am really confused now... I would be glad about any help!</p>