# mthom / scryer-prolog Public

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2021-12-06 22:00:09

A longstanding collaboration with @triska, applying Prolog to the specification and analysis of oncology dose-escalation trial designs, has yielded some interesting progress that I'd like to share. It seems to me that the current thrust of this work employs Prolog — and indeed some new techniques like if_/3 (Neumerkel & Kral 2017) — in a manner that is somehow essential to our successful attack on certain problems in this field. As such, I think our collaboration reflects in some interesting ways on Prolog itself. I'll try to get across the main gist of the application without belaboring the details. The work I'll describe here is contained in this self-contained file from the precautionary package.

Dose-finding studies in oncology continue to follow the inveterate '3+3' design described with a DCG in this arXiv paper and also demonstrated 'organically' in this video. This holds despite 3 decades of effort (mainly by biostatisticians) to replace 3+3 with statistically more sophisticated designs. Thus, for all its many deficiencies, the 3+3 design continues to enjoy 'mind-share' with oncology trialists. Consequently, a demonstration that 3+3 naturally generalizes toward more desirable designs, could potentiate progress in this field. This would be especially true, if a domain-specific language (DSL) could be developed, enabling trialists' direct exploration of this larger class.

Because of its perverse nature, this '3+3' design has long been regarded by dose-finding methodologists as a kind of 'other' — an aberrant and pathological design supposedly bearing no relation to the newer innovations. Markus and I have however already shown that the 3+3 is in fact a special case of a larger class of designs, and indeed of the Bayesian Optimal INterval (BOIN) designs, which are a leading contender among the design classes under active research development.