Survival analysis of hierarchical learning curves in assessment of cardiac device and procedural safety.
Survival analysis of hierarchical learning curves in assessment of cardiac device and procedural safety.
复制标题
心脏装置和手术安全评估中分层学习曲线的生存分析。
DOI：
10.1002/sim.7906
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发表时间：
2018
影响因子：
2
通讯作者：
Resnic,Frederic
中科院分区：
文献类型：
--
作者：
Govindarajulu,Usha;Bedi,Sandeep;Kluger,Aaron;Resnic,Frederic
许多美国人依靠心脏外科手术和设备，如起搏器和溶栓导管来治疗或控制他们的心血管疾病。然而，这些心脏装置和手术的失败可能会产生严重的后果。心脏装置失效的一个原因是医生的失误；对于医生或操作员来说，在熟练地植入设备和执行程序方面，这是一种学习效果。为了更好地理解这些学习效应，我们之前使用我们独特的方法在模拟分层设置中模拟了由此产生的学习曲线效应，其中医生聚集在机构内（参见Govindarajulu等人2017年的工作）。以前，我们已经在层次线性建模和广义估计方程中使用了这些。在这种情况下，我们展示了如何应用类似的方法，但在生存分析框架或时间-事件分析中进行了修改。通过模拟和真实数据集应用，我们发现，在拟合学习曲线的三种形状中，对数形状往往具有最佳拟合，与之前的工作相似（参见Govindarajulu等人2017年的工作）。然而，如前所述，学习率的建模可以是特定于数据集的，一种形状可能比另一种形状更好。我们了解到，通过这种新方法，学习率建模也可以应用于生存分析设置。本文的目标是在时间-事件设置中模拟心脏装置和程序学习曲线效应，以便这些知识可以改善患者的短期和长期生存。
Many Americans rely on cardiac surgical procedures and devices such as pacemakers and thrombolytic catheters to treat or manage their cardiovascular diseases. However, the failure of these cardiac devices and procedures could have grave consequences. One reason cardiac devices tended to fail was due to physician error; there is a learning effect for the physician or operator to come up to speed in skillfully implanting devices and conducting procedures. In order to better understand these learning effects, we had previously modeled the resulting learning curve effects in simulations a hierarchical setting with physicians clustered within institutions using our unique methodology (see the work of Govindarajulu et al 2017). Previously, we had employed these in hierarchical linear modeling and also in generalized estimating equations. In this setting, we have demonstrated how to apply similar methodology but revised in a survival analytic framework or time‐to‐event analyses. Through simulations and real dataset applications, we found that, out of the three shapes modeled to fit the learning curve, the logarithmic shape tended to have the best fit, similar to previous work (see the work of Govindarajulu et al 2017). However, as seen before, modeling the learning rate can be dataset specific and one shape may be better than another. We learned that modeling the learning rate could also be applied in the survival analysis setting through this new methodology. The goal of this paper is to model cardiac device and procedure learning curve effects in a time‐to‐event setting so that this knowledge may allow for the improvement of both short and long‐term patient survival.