Safety data from randomized controlled trials: applying models for recurrent events
Safety data from randomized controlled trials: applying models for recurrent events
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DOI：
10.1002/pst.1757
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发表时间：
2016-07-01
影响因子：
1.5
通讯作者：
Leverkus, Friedhelm
中科院分区：
文献类型：
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作者：
Hengelbrock, Johannes;Gillhaus, Johanna;Leverkus, Friedhelm
基于2x2表格的简单描述性列表和推断统计仍然是总结和报告随机对照试验不良事件数据的最常用方法，尽管这些方法不能解释治疗组之间观察时间的差异。使用生存分析的标准方法（如考克斯模型或Kaplan-Meier估计值）可以克服这一问题，但将分析限制在每例受试者的首起安全性相关事件。作为替代方案，我们讨论了两个模型的复发事件数据Andersen-Gill和Prentice-Williams-Peterson模型关于其适用性的安全性数据从随机对照试验。我们认为，这些模型可用于估计两个不同的数量：对事件风险的直接治疗效果（Prentice-Williams-Peterson）和作为直接效果和治疗通过事件历史的间接效果之和的总治疗效果（Anderson-Gill）。使用模拟数据，我们说明了这些治疗效果之间的差异，并分析了两种模型在不同情况下的性能。由于如果存在竞争风险，则两种模型均限于原因特异性危害的分析，因此我们建议在分析中纳入事件平均频率的估计值，以额外比较治疗对绝对事件概率的影响。我们证明了这两个模型和平均频率函数的应用安全性终点的说明性分析的数据，从一个随机的III期研究。版权所有（c）2016约翰威利父子有限公司
Simple descriptive listings and inference statistics based on 2x2 tables are still the most common way of summarizing and reporting adverse events data from randomized controlled trials, although these methods do not account for differences in observation times between treatment groups. Using standard methods from survival analysis such as the Cox model or Kaplan-Meier estimates would overcome this problem but limit the analysis to the first safety-related event of each subject. As an alternative, we discuss two models for recurrent events datathe Andersen-Gill and Prentice-Williams-Peterson modelregarding their applicability to safety data from randomized controlled trials. We argue that these models can be used to estimate two different quantities: a direct treatment effect on the risk of an event (Prentice-Williams-Peterson) and a total treatment effect as sum of the direct effect and the treatment's indirect effect via the event history (Anderson-Gill). Using simulated data, we illustrate the difference between these treatment effects and analyze the performance of both models in different scenarios. Because both models are limited to the analysis of cause-specific hazards if competing risks are present, we suggest to incorporate estimates of the mean frequency of events in the analysis to additionally allow the comparison of treatment effects on absolute event probabilities. We demonstrate the application of both models and the mean frequency function to safety endpoints with an illustrative analysis of data from a randomized phase-III study. Copyright (c) 2016 John Wiley & Sons, Ltd.