论文标题
具有时变系数和未观察的异质性的柔性但稀疏的贝叶斯生存模型
Flexible yet Sparse Bayesian Survival Models with Time-Varying Coefficients and Unobserved Heterogeneity
论文作者
论文摘要
生存分析是医学研究的重要领域，但是现有的模型通常难以在简单和灵活性之间取得平衡。简单的模型需要最少的调整，但具有强大的假设，而更灵活的模型则需要研究人员的大量输入和调整。我们使用贝叶斯分层收缩法提出了生存模型，该方法会自动确定每个协变量应被视为静态，时间变化或完全排除。这种方法达到了简单性和灵活性之间的平衡，最大程度地减少了调整的需求，并自然量化了不确定性。该方法由在R软件包ShrinkDSM中实现的有效的Markov链蒙特卡洛采样器支持。与现有模型相比，全面的仿真研究和应用于涉及胃食管腺癌患者的临床数据集的应用，展示了我们方法的优势。
Survival analysis is an important area of medical research, yet existing models often struggle to balance simplicity with flexibility. Simple models require minimal adjustments but come with strong assumptions, while more flexible models require significant input and tuning from researchers. We present a survival model using a Bayesian hierarchical shrinkage method that automatically determines whether each covariate should be treated as static, time-varying, or excluded altogether. This approach strikes a balance between simplicity and flexibility, minimizes the need for tuning, and naturally quantifies uncertainty. The method is supported by an efficient Markov chain Monte Carlo sampler, implemented in the R package shrinkDSM. Comprehensive simulation studies and an application to a clinical dataset involving patients with adenocarcinoma of the gastroesophageal junction showcase the advantages of our approach compared to existing models.
