Change point estimation in monitoring survival time: average of 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 the average of posterior estimates (mode, sd.) of step change point model parameters ( and ) for a change in the mean survival time following signals (RL) from RAST CUSUM () where and .

本数据集旨在针对存在患者构成混杂情况、结局为二分类结局（dichotomous outcomes）的临床过程，构建事件发生时间数据（time-to-event data）中的变点估计模型。建模工作采用贝叶斯框架（Bayesian framework）完成。本研究通过模拟实验结合RAST累积和控制图（RAST CUSUM control charts），对随访周期为30天内接受心脏手术的患者的右删失生存时间（right censored survival time）进行监测，以此评估贝叶斯估计量（Bayesian estimators）的性能。

本数据集包含了当RAST累积和控制图（RAST CUSUM）发出信号（RL）后，平均生存时间发生变化的阶跃变点模型参数（与）的后验估计（posterior estimates）均值（众数、标准差（sd.）），其中与。