Change point estimation in monitoring survival time: average of detected time of a step change in the mean survival time

The dataset was collected to model change point estimation in time-to-event data for a clinical process with dichotomous outcomes, death and survival, where patient mix was present. Modelling was completed using a Bayesian framework. The performance of the Bayesian estimators was investigated through simulation in conjunction with RAST CUSUM control charts for monitoring right censored survival time of patients who underwent cardiac surgery procedures within a follow-up period of 30 days.

The dataset presents the average of detected time of a step change in the mean survival time obtained by the Bayesian estimator () and CUSUM built-in estimator following signals (RL) from RACUSUM () where and .

该数据集的收集旨在模拟临床过程中具有二分类结果（死亡与存活）的时间至事件数据中的变化点估计，其中患者构成存在差异。模拟工作在贝叶斯框架下完成。通过模拟研究以及结合 RAST 累积和控制图（CUSUM）对接受心脏手术的患者在30天随访期内的右 censor 生存时间进行监测，对贝叶斯估计器的性能进行了探究。数据集展示了贝叶斯估计器获得的平均生存时间步长变化以及继 RACUSUM 信号（RL）之后的 CUSUM 内置估计器的平均值。