提出可解释的分层阈值框架，提升生物标志物决策的稳定性和可复现性。
Hierarchical biomarker thresholding: a model-agnostic framework for stability
- 基于风险分解定理，分离模型拟合、预估值变化与稳定性三部分贡献。
- 通过患者块自助法计算稳定性项，有效应对数据分布差异和阈值敏感问题。
- 适用于多种模型，输出可报告的诊断指标，适合临床决策支持系统。
许多生物标志物分析流程需要将个体（细胞/局部区域）评分聚合为患者级决策。在整体数据上调优的阈值常因层级依赖、流行率偏移和评分尺度不一致而在不同机构间失效。本文提出一种选择无偏的分层阈值框架，使患者级决策更具可复现性与可辩护性。核心是选择无偏阈值的风险分解定理，将贡献拆分为：(i) 内部拟合与患者级泛化能力，(ii) 操作点偏移（反映流行率与分布形状变化），(iii) 稳定性项，惩罚对阈值微小扰动的敏感性。稳定性项可通过患者块自助法结合单调风险模量进行计算。该框架模型无关，可在分位数尺度上统一异构决策规则，生成单调不变的集成结果及可报告诊断指标（如翻转率、操作点偏移）。
原文摘要 · Abstract (English)
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).
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