Very nice. I run a wet lab (bench work and animal modeling) where we generally formally focus more on p(s’|a,s) in your notation, i.e., in making inferences/assertions ourselves or sharing the data with the community to make their own assertions. In clinic, we also mainly focus on p(s’|a,s) although we argue here why utilities are essential for patient-specific decision making.
Our use of utilities is more extensive in designing and running clinical trials. The first trial I ever designed and ran used a proto-version of utilities for dose selection based on efficacy and toxicity. We now have multiple utility-based trial designs, many of which are easily accessible for practical use here. As described here, we advocate for the utility functions themselves to be covariate specific as in this phase I-II design and this phase II design. We provide an example of covariate specific utility function elicitation for breast cancer decision-making here.
Definitely supportive of popularizing dynamic treatment regimens in medicine. Our group designed and run what may have been the first sequential multiple assignment randomized trial (SMART) in oncology (example discussion of the trial here) and have been analyzing the first SMART in kidney cancer for years (not an easy task due to challenges such as non-compliance etc). Hopefully we will publish it in 2025. The purpose of this datamethods post was exactly to showcase the necessity of thinking in terms of treatment regimes in oncology. We accordingly just published with the Kidney Cancer Association a consensus statement on this topic.