We describe Arbalest, A minimal CLI and checklist that locks in a subgroup-analysis plan before data unblinding and flags any post-hoc additions.. Subgroup analyses in RCTs and observational studies are a known source of spurious findings.
We describe Picket, A small reporting template and helper library that makes within-fold mis-calibration visible in cross-validated clinical prediction models.. Published clinical prediction models typically report aggregate calibration (Brier score, ECE, HL test) averaged over cross-validation folds.
Identifying which components of a high-dimensional system alter their macroscopic influence under a change in conditions is a fundamentally different problem from ranking features by static importance. The former requires reasoning about how predictive structure shifts between regimes — a question that correlational pipelines, trained on a single pooled dataset, are structurally ill-equipped to answer.
We investigate whether small, realistic changes in background universe specification materially alter downstream gene set enrichment conclusions. Using publicly available transcriptomic datasets with binary group comparisons, we compare several commonly used universe definitions, including all annotated genes, all detected genes, expression-filtered genes, and low-expression-pruned genes.