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The Effect of Estimation Error on Risk-Adjusted Survival Time CUSUM Chart Performance

机译:估计误差对风险调整后生存时间CUSUM图表性能的影响

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Research on risk-adjusted control charts has gained great interest in healthcare settings. Based on monitored variables (binary outcome or survival times), risk-adjusted cumulative sum (CUSUM) charts are divided into Bernoulli and survival time CUSUM charts. The effect of estimation error on control chart performance has been systematically studied for Bernoulli CUSUM but not for survival time CUSUM in continuous time. We investigate the effect of estimation error on the performance of risk-adjusted survival time CUSUM scheme in continuous time with the cardiac surgery data. The impact is studied with the use of the median run lengths (medRLs) and the standard deviation (SD) of medRLs for different sample sizes, specified in-control median run length, adverse event rate and patient variability. Results show that estimation error affects the performance of risk-adjusted survival time CUSUM chart significantly and the performance is more sensitive to the specified in-control median run length (medRL(0)) and adverse event rate. To take the estimation error into account, the practitioners can bootstrap many samples from Phase I data and then determine the threshold that can guarantee at least a medRL(0) with certain probability under which false alarms occur less frequently and meanwhile out-of-control alarms don't signal too slow. Moreover, additional event occurrences can be used to update the estimation but should be from in-control process. Finally, non-parametric bootstrap can be applied to reduce model misspecification error. Copyright (c) 2015 John Wiley & Sons, Ltd.
机译:对风险调整后的控制图的研究引起了医疗机构的极大兴趣。基于监视变量(二进制结果或生存时间),风险调整后的累积总和(CUSUM)图分为伯努利图和生存时间CUSUM图。对于Bernoulli CUSUM,已经系统地研究了估计误差对控制图性能的影响,但对于连续时间中的生存时间CUSUM,则没有进行研究。我们用心脏手术数据调查估计误差对风险调整生存时间CUSUM方案在连续时间内的性能的影响。使用不同样本量,指定的对照中位数游程长度,不良事件发生率和患者变异性的中位数游程长度(medRLs)和medRLs的标准差(SD)研究影响。结果表明,估计误差会显着影响风险调整后的生存时间CUSUM图表的性能,并且该性能对指定的对照中位数游程长度(medRL(0))和不良事件发生率更加敏感。为了将估计误差考虑在内,从业人员可以从阶段I数据中引导许多样本,然后确定可以至少以一定概率保证medRL(0)的阈值,在该阈值下错误警报的发生频率较低,同时失控警报不会太慢。此外,可以使用其他事件发生来更新估计,但是应该来自控制中过程。最后,可以应用非参数引导程序来减少模型指定错误。版权所有(c)2015 John Wiley&Sons,Ltd.

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