发表机构
University of California, San Francisco; Dalian Institute of Chemical Physics, Chinese Academy of Sciences; University of Cincinnati; Duke University（旧金山加利福尼亚大学; 中国科学院大连化学物理研究所; 辛辛那提大学; 杜克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对中小样本量下精准肿瘤学的风险预测挑战，本文提出基于Kullback-Leibler散度整合外部模型预测的贝叶斯生存分析框架，经模拟与前列腺癌数据验证可提升估计效率并校正覆盖过度问题。
AI 中文摘要
预后因素评估和预测模型开发是精准肿瘤学的核心，可实现患者风险分层与个体化治疗选择。综合现有模型信息的统一预测对全面、一致的风险评估具有重要价值，许多研究还旨在评估新型生物标志物在既定预后因素之外的增量价值，但此类努力常受限于中小样本量。受这些挑战驱动，我们考虑在现有模型的个体化风险预测可外部获取且无透明或可解释结构（例如通过在线计算器）的情况下开展Cox回归分析。我们开发了一种贝叶斯离散化生存时间推断框架，其中来自潜在多个外部来源的个体化预测通过基于Kullback-Leibler散度的公式进行整合，从而生成信息先验。该基于散度的公式作为外部信息似然的替代，使我们能够在无需了解底层外部预测模型的情况下，合理纳入个体化预测。理论结果表明，所得后验均值估计量在渐近意义上比仅使用内部数据的最大似然估计量更有效，但用基于散度的替代物替代不可用的外部似然会导致基于后验方差的推断偏保守，我们提出了一种校正方法来解决这种覆盖过度的问题。我们通过模拟和对前列腺癌试验数据的应用验证了所提方法的性能。
英文摘要
Prognostic factor evaluation and prediction model development are central to precision oncology, enabling patient risk stratification and individualized treatment selection. Unified predictions that synthesize information from existing models are valuable for comprehensive and consistent risk assessment. Many studies also seek to evaluate the incremental value of new biomarkers beyond established prognostic factors. However, such efforts are often constrained by small-to-moderate sample sizes. Motivated by these challenges, we consider Cox regression analysis in settings where individualized risk predictions from existing models are externally available without a transparent or interpretable structure, for example, through online calculators. We develop a Bayesian discretized survival time inference framework in which individualized predictions from potentially multiple external sources are integrated through a formulation based on Kullback-Leibler divergence, yielding informative priors. The divergence-based formulation serves as a surrogate for the external information likelihood, enabling principled incorporation of individualized predictions without requiring knowledge of the underlying external prediction models. Theoretical results show that the resulting posterior mean estimators are asymptotically more efficient than their internal-only maximum likelihood counterparts. However, using the divergence-based surrogate in place of the unavailable external likelihood renders posterior variance-based inference conservative. We propose a correction to address this overcoverage. We demonstrate the performance of the proposed approach through simulations and an application to prostate cancer trial data.
CommentsManin paper: 28 pages, 1 figure, 3 tables. Supplementary material: 26 pages
