Michimae Hirofumi, Emura Takeshi, Miyamoto Atsushi, Kishi Kazuma
School of Pharmacy, Department of Clinical Medicine (Biostatistics), Kitasato University, Tokyo, Japan.
Biostatistics Center, Kurume University, Kurume, Japan.
J Appl Stat. 2024 Feb 22;51(13):2690-2708. doi: 10.1080/02664763.2024.2315458. eCollection 2024.
In observational/field studies, competing risks and left-truncation may co-exist, yielding 'left-truncated competing risks' settings. Under the assumption of independent competing risks, parametric estimation methods were developed for left-truncated competing risks data. However, competing risks may be dependent in real applications. In this paper, we propose a Bayesian estimator for both independent competing risks and copula-based dependent competing risks models under left-truncation. The simulations show that the Bayesian estimator for the copula-based dependent risks model yields the desired performance when competing risks are dependent. We also comprehensively explore the choice of the prior distributions (Gamma, Inverse-Gamma, Uniform, half Normal and half Cauchy) and hyperparameters via simulations. Finally, two real datasets are analyzed to demonstrate the proposed estimators.
在观察性/实地研究中，竞争风险和左截断可能同时存在，产生“左截断竞争风险”情况。在独立竞争风险的假设下，针对左截断竞争风险数据开发了参数估计方法。然而，在实际应用中竞争风险可能是相关的。在本文中，我们针对左截断情况下的独立竞争风险模型和基于 copula 的相关竞争风险模型提出了一种贝叶斯估计器。模拟结果表明，当竞争风险相关时，基于 copula 的相关风险模型的贝叶斯估计器具有理想的性能。我们还通过模拟全面探讨了先验分布（伽马分布、逆伽马分布、均匀分布、半正态分布和半柯西分布）和超参数的选择。最后，分析了两个真实数据集以展示所提出的估计器。
