# Hierarchical biomarker thresholding: a model-agnostic framework for stability

arXiv: https://arxiv.org/abs/2511.18030 · HTML: https://mlsanity.com/paper/2511.18030/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 22 Nov 2025.

## Builds on (most cited first, 4 of 4)

- [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://mlsanity.com/paper/2107.07511/) (2021)
- [Detecting and Correcting for Label Shift with Black Box Predictors](https://mlsanity.com/paper/1802.03916/) (2018)
- [Learning Models with Uniform Performance via Distributionally Robust Optimization](https://mlsanity.com/paper/1810.08750/) (2018)
- [Optimal Inference After Model Selection](https://mlsanity.com/paper/1410.2597/) (2014)

## Abstract (arXiv)

Many biomarker pipelines require patient-level decisions aggregated from instance-level (cell/patch) scores. Thresholds tuned on pooled instances often fail across sites due to hierarchical dependence, prevalence shift, and score-scale mismatch. We present a selection-honest framework for hierarchical thresholding that makes patient-level decisions reproducible and more defensible. At its core is a risk decomposition theorem for selection-honest thresholds. The theorem separates contributions from (i) internal fit and patient-level generalization, (ii) operating-point shift reflecting prevalence and shape changes, and (iii) a stability term that penalizes sensitivity to threshold perturbations. The stability component is computable via patient-block bootstraps mapped through a monotone modulus of risk. This framework is model-agnostic, reconciles heterogeneous decision rules on a quantile scale, and yields monotone-invariant ensembles and reportable diagnostics (e.g. flip-rate, operating-point shift).