Clinically Relevant Risk Factors - Survival Analysis

I’m currently working with some clinicians on exploring the role of a specific risk factor for survival in an oncology trial. I know there has been discussion about clinically meaningful effects for treatments, but I haven’t seen any discussion about a similar concept for risk factors.

Has anyone come across any guidance on how to choose/define a clinically meaningful impact of a risk factor in a model? I think one of the main additional considerations would be prevalence or variability of the risk factor.

I am using methods similar to those described here to actually see if the predictor is ‘useful’, i.e, improves the model. I plan on using the R^2 to describe improvement in the model, but it’s not clear how to turn that into a clinically meaningful measure.

Specifically, there is a list of prognostic markers that are most likely relevant in non small cell lung cancer:

  1. Age
  2. PDL1
  3. Smoking status etc.

We want to see how useful a new biomarker (for example TMB - a continuous variable) is in predicting survival. I’ll be able to do that using a likelihood ratio test. However, how could you communicate the additional usefulness of this variable in a risk model?

Thank you in advance

Not sure if this is what you want but take a look at Statistically Efficient Ways to Quantify Added Predictive Value of New Measurements | Statistical Thinking

Thank you - this is definitely down the right path. The publication you link to Fronkzec 2021 is helpful. Have you come across anything that does this for survival analyses? Perhaps I can use something like RMST at a fixed value or something like that.

Most of those measures work quite generally including for censored Y.