发表机构
Institute of Statistical Science, Academia Sinica（中央研究院统计科学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对罕见事件风险预测中阈值决策问题，提出面向决策的序列验证方法，通过调整预测带评估决策，经模拟和实际数据验证该方法可区分模型可估计性与决策可验证性。
AI 中文摘要
罕见临床结局带来的困难远超普通类别不平衡问题：在数据支持可靠阈值决策前，惩罚逻辑模型可能返回有限、看似稳定的系数。我们将累积问题表述为面向决策的序列验证。在预先指定的有限监测时间表和目标集上，调整渐近预测带以实现同时覆盖，且仅在可能被转诊的个体中评估验证，从而避免大量低风险多数群体触发无信息的停止。在工作罕见事件逻辑模型和所述正则条件下，每个经验证的决策在时间表上以至少1-α的概率渐近为模型条件正确，且有效信息随真实事件数量缩放。模拟研究和美国关联出生/婴儿死亡应用示例表明，仅可估计的模型与决策可验证的模型之间存在差距：全人群规则停止时，超过一半与转诊相关的个体仍处于模糊状态，而面向决策的规则尽管时间验证稳定，但在30万出生的时间范围内仍未完成验证。正则化可使罕见事件模型可估计，但无法替代真实的罕见事件信息。
英文摘要
Rare clinical outcomes pose a difficulty deeper than ordinary class imbalance: a penalized logistic model can return finite, stable-looking coefficients before the data support a reliable threshold decision. We formulate the accrual question as decision-targeted sequential certification. On a prespecified finite monitoring schedule and target set, asymptotic prediction bands are adjusted for simultaneous coverage, and certification is assessed only among profiles that might be referred, so that a large low-risk majority cannot trigger an uninformative stop. Under the working rare-event logistic model and stated regularity conditions, each certified decision is asymptotically model-conditionally correct with probability at least \(1-α\) over the schedule, and the effective information scales with the number of genuine events. Simulations and a US linked birth/infant-death application illustrate the gap between a model that is merely estimable and one whose decisions are certifiable: the whole-population rule stopped while more than half of referral-relevant profiles remained ambiguous, whereas the decision-targeted rule did not certify by the 300{,}000-birth horizon despite stable temporal validation. Regularization makes a rare-event model estimable but does not substitute for genuine rare-event information.
Comments29 pages, 7 figures
