Why not? When making individualized inferences we have to assume that the patient we see in clinic shares certain stable causal properties with the RCT population. One could indeed assume that this causal structure is only present in the RCT cohort and apply the RCT findings only to patients from the exact same population, including the center they were treated and all other eligibility criteria. But no clinician ever does that, certainly not in oncology. Instead, we assume that the patient we see in clinic shares the same key causal structures that allow the transportability of inferences from an RCT that may, for example, have been done in a different country. This is a reasonable approach, in part because the RCT eligibility criteria are often very poor discriminators of patient baseline risk, as opposed to large-scale observational studies to develop prognostic risk scores.
But now note that the assumptions needed to transport RCT inferences to the patient we see in clinic are generally stronger than the ones needed to integrate knowledge on the relative treatment effect from RCTs with knowledge on baseline risks from observational datasets.
I am writing a paper on this with the hope that it will be accessible to statisticians and methodology-minded clinicians. The biggest challenge is notation. Each community focuses on different aspects of a problem and uses their own terminology and mathematical notation, and this can be confusing. I was raised within the Bayesian statistical school and I am much more familiar with that notation. But for this and other related projects, in the past few months I dived much more into the notation used by computer scientists to also help our statistical collaborators digest and incorporate it in our work. It is not an easy task but it is well worth it.
As a very simple but practical example of how valuable this integration can be, see this post discussing how problematic it can be to judge a surrogate endpoint based on how strongly it correlates with overall survival. Instead, we need first to consider the causal networks that generated these endpoints.