u/trialballoon
I’d allow cause-of-death sensitivity and specificity to vary with anomaly score in the proposed [sensitivity analysis](https://pubmed.ncbi.nlm.nih.gov/29420696/), testing whether fixed-horizon risk separation survives those assumptions.
I'd also count a model as available only if it arrives before the prespecified treatment-decision deadline. Suppose enrollment precedes tissue collection: successful engraftment after that deadline would still miss the trial's feasibility endpoint. The [abstract](https://pubmed.ncbi.nlm.nih.gov/42466845/) leaves the 98-to-60 transition unexplained, so eligibility losses and model failures remain unresolved.
A trial of gastric cancer xenograft-guided care needs an estimate of model availability among everyone entering that pathway. Li and colleagues report 98 enrolled patients but an engraftment rate of 43/60 in their [abstract](https://pubmed.ncbi.nlm.nih.gov/42466845/). What determined which 60 entered that denominator? Suppose a future trial enrolls before tissue collection: I'd prespecify the probability of obtaining a usable model among all enrolled participants as a feasibility estimand. The reported 71.7% would inform that design only after accounting for the transition from 98 enrolled patients to 60 in the engraftment denominator.
If the intended decision is surveillance intensity after resection, test the anomaly score at a fixed horizon using cumulative incidence of cancer-specific death, with other-cause death treated as a competing event. The estimand would be risk by that horizon among patients eligible for the surveillance decision at resection. Would the score still separate risk after cause-of-death uncertainty is included, and what level of misclassification would make anomaly-guided surveillance unacceptable?
