AI 中文总结
研究特定疾病合并症指数开发中标准乐观校正的问题，提出选择感知自举法，通过在重采样中重新运行最佳选择消除结构偏差，经模拟和实验验证该方法能有效改进置信区间，提高判别准确性。
AI 中文摘要
特定疾病的合并症指数通常通过构建多个候选结构并报告得分最高的结构来开发，然后声称它比像查尔森或埃利克斯豪泽评分这样的现成固定比较器具有更高的判别价值。我们表明，标准使用中的乐观校正并不能使该声明有效。因为它将所选模型当作是唯一拟合的模型进行校正，从而忽略了从多个候选模型中选择最佳模型时的胜者诅咒项；所以其增量一致性的置信区间不仅在小样本中过于乐观，而且在结构上校准错误，并且不会随着样本量的增加而缩小。在真正的零假设下，它会使高于名义水平的附加值的错误声明膨胀，随着筛选的候选模型增多，这种情况会越来越严重。我们引入了一种即插即用的选择感知自举法，在每个重采样中，在保持比较器固定的情况下重新运行多个最佳选择，消除结构偏差。在一个完全已知真实情况的模拟中，标准校正下的95%覆盖率从有一个候选模型时的0.94降至有一百个候选模型时的0.70，而选择感知区间接近名义水平；其覆盖率与校准的交叉验证区间匹配，并且在匹配的错误率下至少同样强大。结果在尤诺一致性下成立，对真实调查数据的半合成实验证实了校正何时重要。在实践中，如果尝试了多个结构，报告一个选择感知区间，这在有许多质量相似的候选模型且每个候选模型事件较少时最为需要。范围仅为判别；软件和结果重现了每一个发现。
英文摘要
Disease-specific comorbidity indices are routinely developed by building several candidate constructions and reporting the best-scoring one, then claiming it adds discriminative value over a fixed off-the-shelf comparator such as the Charlson or Elixhauser score. We show that the optimism correction in standard use does not make that claim valid. Because it corrects the selected model as if it were the only one ever fit, it omits the winner's-curse term from choosing the best of several candidates; so its confidence interval for the incremental concordance is not merely optimistic in small samples but structurally miscalibrated, and does not shrink as the sample grows. At a true null it inflates false claims of added value above the nominal level, increasingly so as more candidates are screened. We introduce a drop-in selection-aware bootstrap that re-runs the best-of-several selection inside each resample with the comparator held fixed, removing the structural bias. In a fully-known-truth simulation, 95% coverage under the standard correction falls from 0.94 with one candidate to 0.70 with a hundred, while the selection-aware interval holds near nominal; its coverage matches a calibrated cross-validation interval, and at a matched error rate it is at least as powerful. The results hold under Uno's concordance, and a semi-synthetic experiment on real survey data confirms when the correction matters. In practice, if several constructions were tried, report a selection-aware interval, most needed with many similar-quality candidates and few events per candidate. The scope is discrimination only; software and results reproduce every finding.
Comments24 pages, 8 tables, 1 figure; 40-page Web Appendix included as an ancillary file. Code and results: https://github.com/ehsanx/selection-aware-cindex
