无目标结局时生存模型比较的锐化部分识别
Sharp Partial Identification for Survival Model Comparison Without Target Outcomes
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中文总结 AI 辅助
本文提出在目标结局不可得时比较两个生存预测模型的锐化部分识别方法，通过有界偏移模型和直接识别收紧风险对比界限，模拟与NSCLC应用中验证其能降低不确定性并影响操作决策。
中文摘要 AI 辅助
我们在目标生存结局可用之前，比较目标人群中的两个锁定生存预测模型。在预设的时间点上，估计目标是目标布里尔风险对比。在预设部署摘要上的有界条件对数几率偏移模型下，我们推导出一个锐化识别集，该集合保留了共享的未识别目标结局分布。直接识别从不宽于分别识别风险并减去其界限，且在布里尔特定的同侧1/2条件下严格收紧。对于右删失源数据，条件Cox删失估计、逆删失概率加权逻辑结局建模以及联合配对自助法在有限敏感性网格上产生同时置信包络。在模拟中，跨受控预测几何的单独到直接宽度比范围为1.00至5.73。针对性模拟显示，在小规模、重度删失的非小细胞肺癌（NSCLC）信息尺度上，外包络的有限样本覆盖不足（0.847-0.861对比名义0.95），而在Rotterdam-GBSG尺度上为0.946。在跨机构NSCLC应用中，尽管识别不确定性降低，所有40项预设评估均导致DEFER。在支持性的Rotterdam到GBSG分析中，候选优越性在小敏感性允许下得到认证；一个锁定配置在直接识别下产生ADOPT CANDIDATE，但在分别风险减法下产生DEFER。当信号和采样精度足够时，直接识别能实质性减少识别不确定性并改变操作结论，而在方向性认证不受支持时保留DEFER。
英文摘要
We compare two locked survival prediction models in a target population before target survival outcomes are available. At a prespecified horizon, the estimand is the target Brier-risk contrast. Under a bounded conditional log-odds shift model on a prespecified deployment summary, we derive a sharp identified set preserving the shared unidentified target outcome law. Direct identification is never wider than separately identifying the risks and subtracting their bounds, with strict tightening under a Brier-specific same-side-1/2 condition. For right-censored source data, conditional Cox censoring estimation, inverse-probability-of-censoring-weighted logistic outcome modeling, and a joint pairs bootstrap yield simultaneous confidence envelopes over a finite sensitivity grid. In simulations, separate-to-direct width ratios ranged from 1.00 to 5.73 across controlled prediction geometries. Targeted simulations showed finite-sample undercoverage of the outer envelope at the small, heavily censored non-small-cell lung cancer (NSCLC) information scale (0.847-0.861 versus 0.95 nominal), compared with 0.946 at the Rotterdam-GBSG scale. In the cross-institutional NSCLC application, all 40 prespecified evaluations resulted in DEFER despite reduced identification uncertainty. In a supporting Rotterdam-to-GBSG analysis, candidate superiority was certified under small sensitivity allowances; one locked configuration yielded ADOPT CANDIDATE under direct identification but DEFER under separate-risk subtraction. Direct identification can materially reduce identification uncertainty and change the operational conclusion when signal and sampling precision are sufficient, while retaining DEFER when directional certification is unsupported.
发表机构
- Chonnam National University（全南国立大学）
机构由 AI 辅助整理，请以论文原文为准。
