A patient similarity-embedded Bayesian approach to prognostic biomarker inference with application to thoracic cancer immunity
提出一种结合机器学习和贝叶斯建模的统计方法，通过患者相似性嵌入识别局部预后模式，用于非小细胞肺癌肿瘤微环境中抗癌免疫标志物的预后价值评估，优于传统回归和机器学习模型。
This paper introduces a novel statistical methodology integrating machine learning (ML) and Bayesian modelling to facilitate personalized prognostic predictions with application to oncology. Utilizing power priors, we construct 'patient-similarity embeddings' that identify localized patterns of prognosis. The methodology is applied to study the prognostic value of markers of anticancer immunity within the tumour microenvironment of nonsmall cell lung cancer while adjusting for established clinical characteristics. The method outperforms traditional regression and ML models, while accurately identifying subgroup patterns, thereby enhancing statistical inference and hypothesis testing.