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Estimation of the time of a linear trend in monitoring survival time

机译：估计生存时间中线性趋势的时间

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Change point estimation is recognized as an essential tool of root cause analyses within quality control programs as it enables clinical experts to search for potential causes of change in hospital outcomes more effectively. In this paper, we consider estimation of the time when a linear trend disturbance has occurred in survival time following an in-control clinical intervention in the presence of variable patient mix. To model the process and change point, a linear trend in the survival time of patients who underwent cardiac surgery is formulated using hierarchical models in a Bayesian framework. 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. We use Markov Chain Monte Carlo to obtain posterior distributions of the change point parameters including the location and the slope size of the trend 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 cumulative sum control chart (CUSUM) control charts for different trend scenarios. In comparison with the alternatives, step change point model and built-in CUSUM estimator, more accurate and precise estimates are obtained by the proposed Bayesian estimator over linear trends. These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered.
机译：变更点估计被认为是质量控制程序中根本原因分析的重要工具，因为它使临床专家能够更有效地寻找医院结果变更的潜在原因。在本文中，我们考虑了在存在可变患者组合的情况下，在有控制的临床干预后，生存时间发生线性趋势扰动的时间的估计。为了对过程和变化点进行建模，在贝叶斯框架中使用分层模型制定了接受心脏手术的患者的生存时间的线性趋势。由于监控是在有限的后续时间内进行的，因此对数据进行了正确的检查。我们使用Weibull加速失败时间回归模型在手术前捕获危险因素的影响。我们使用马尔可夫链蒙特卡罗方法获得变化点参数的后验分布，包括趋势的位置和斜率大小以及相应的概率区间和推论。通过仿真研究了贝叶斯估计量的性能，结果表明，将它们与风险调整的生存时间累积总和控制图（CUSUM）控制图结合使用时，可以获得不同趋势情况下的精确估计。与替代方案，阶跃变化点模型和内置CUSUM估计器相比，建议的贝叶斯估计器在线性趋势上获得了更准确，更精确的估计。当同时考虑贝叶斯变化点检测模型的概率量化，灵活性和可概括性时，这些优势将得到增强。

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