Change point estimation in monitoring survival time: posterior estimates of step change point model parameters

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 posterior estimates (mode, sd.) of step change point model parameters ( and ) following signals (RL) from RAST CUSUM () where and .

该数据集的收集旨在对具有二元结果（死亡和存活）的临床过程的时间至事件数据中的变化点进行建模，其中存在患者构成差异。建模工作是在贝叶斯框架下完成的。通过模拟结合 RAST CUSUM 控制图对贝叶斯估计器的性能进行了研究，以监控在30天随访期内接受心脏手术的患者被右端截尾的生存时间。数据集展示了后验估计（众数、标准差）的阶跃变化点模型参数（及），这些参数是在接收到 RAST CUSUM （）的信号（RL）之后确定的，其中 及 。