Change point estimation in monitoring survival time following cardiac surgery

Precise identification of the time when a change in a hospital outcome has occurred enables clinical experts to search for a potential special cause more effectively. In our paper, we develop change point estimation methods for survival time of a clinical procedure in the presence of patient mix in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step change in the mean survival time of patients who underwent cardiac surgery.

The data are right censored since the monitoring is conducted over a limited follow-up period. We capture the effect of risk factors prior to the surgery using a Weibull accelerated failure time regression model. Markov Chain Monte Carlo is used to obtain posterior distributions of the change point parameters including location and magnitude of changes and also corresponding probabilistic intervals and inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that precise estimates can be obtained when they are used in conjunction with the risk-adjusted survival time CUSUM control charts for different magnitude scenarios. The proposed estimator shows a better performance where a longer follow-up period, censoring time, is applied. In comparison with the alternative built-in CUSUM estimator, more accurate and precise estimates are obtained by the Bayesian estimator. These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered.

精确识别医院结果变化发生的时间，有助于临床专家更有效地寻求潜在的特定原因。在本文中，我们针对存在患者混杂情况的临床手术生存时间，在贝叶斯框架下开发了一种变化点估计方法。我们应用贝叶斯层次模型来构建存在患者接受心脏手术平均生存时间阶梯变化的变化点。由于监测是在有限的随访期内进行的，数据存在右 censoring。我们使用韦伯尔加速失效时间回归模型来捕捉手术前风险因素的影响。通过马尔可夫链蒙特卡罗方法获得变化点参数的后验分布，包括变化的位置和幅度，以及相应的概率区间和推断。通过模拟研究贝叶斯估计器的性能，结果表明，当与针对不同幅度场景的风险调整生存时间累积和控制图联合使用时，可以获取精确的估计值。所提出的估计器在应用更长的随访期和 censoring 时间时展现出更优的性能。与内置的 CUSUM 估计器相比，贝叶斯估计器获得了更准确和精确的估计。当考虑贝叶斯变化点检测模型的概率量化、灵活性和泛化能力时，这些优势更为显著。