Qiu Mingyue, Hu Tao
School of Mathematical Sciences, Capital Normal University, Beijing, People's Republic of China.
J Appl Stat. 2023 Sep 27;51(11):2139-2156. doi: 10.1080/02664763.2023.2263819. eCollection 2024.
The transformation model with partly interval-censored data offers a highly flexible modeling framework that can simultaneously support multiple common survival models and a wide variety of censored data types. However, the real data may contain unexplained heterogeneity that cannot be entirely explained by covariates and may be brought on by a variety of unmeasured regional characteristics. Due to this, we introduce the conditionally autoregressive prior into the transformation model with partly interval-censored data and take the spatial frailty into account. An efficient Markov chain Monte Carlo method is proposed to handle the posterior sampling and model inference. The approach is simple to use and does not include any challenging Metropolis steps owing to four-stage data augmentation. Through several simulations, the suggested method's empirical performance is assessed and then the method is used in a leukemia study.
具有部分区间删失数据的转换模型提供了一个高度灵活的建模框架，该框架可以同时支持多种常见的生存模型以及各种各样的删失数据类型。然而，实际数据可能包含无法解释的异质性，这种异质性不能完全由协变量来解释，并且可能由各种未测量的区域特征导致。因此，我们将条件自回归先验引入到具有部分区间删失数据的转换模型中，并考虑空间脆弱性。提出了一种有效的马尔可夫链蒙特卡罗方法来处理后验抽样和模型推断。该方法使用简单，并且由于采用了四阶段数据增强，不包括任何具有挑战性的Metropolis步骤。通过几次模拟，评估了所提方法的实证性能，然后将该方法应用于一项白血病研究中。
