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Statistical performance of group sequential methods for observational post-licensure medical product safety surveillance: A simulation study

机译：观察性许可后医疗产品安全性监视的团体顺序法的统计性能：模拟研究

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In order to improve post-licensure drug and vaccine safety surveillance, new systems are being developed that prospectively monitor observational health care data from large health plans. Continuous sequential testing has been proposed in this setting to facilitate rapid detection, but group sequential methods commonly used in randomized clinical trials (RCTs) have received less consideration. We propose a group sequential approach tailored for safety and to account for complications like confounding that arise in this non-randomized setting and thus have not been previously examined in RCTs. For comparability with prior continuous monitoring applications, we use a likelihood ratio statistic and historical controls. We compute sequential boundaries using Monte Carlo simulation and show how they can accommodate unequal between-test sample sizes and changes in confounder distributions among accruing subjects over time. We evaluate via simulation the performance of this approach across sequential designs suited for safety and not previously addressed by simulation studies evaluating RCT boundaries. Such designs include much higher frequency testing and designs that employ early conservatism followed by frequent testing. Contrary to prior RCT simulations, we found major differences in the average time-to-surveillanceend and overall power. We apply this methodology to safety data on a new pediatric combination vaccine.
机译：为了改善许可后的药物和疫苗安全性监视，正在开发新的系统，以对大型医疗计划中的观察性医疗数据进行前瞻性监视。已提出在这种情况下进行连续顺序测试以促进快速检测，但是随机临床试验（RCT）中常用的分组顺序方法受到的考虑较少。我们提出了一种针对安全性而量身定做的小组顺序方法，并考虑了在这种非随机环境中出现的复杂问题，例如混淆，因此以前未在RCT中进行过检查。为了与以前的连续监视应用程序进行比较，我们使用似然比统计和历史控制。我们使用蒙特卡洛模拟计算顺序边界，并显示它们如何适应不等的两次测试之间的样本大小以及随着时间的推移在应计主题之间混杂因素分布的变化。我们通过仿真评估这种方法在适合安全性的顺序设计中的性能，而以前并未通过评估RCT边界的仿真研究解决。此类设计包括更高频率的测试，以及采用早期保守性并随后进行频繁测试的设计。与先前的RCT模拟相反，我们发现平均监控时间和总体功率存在重大差异。我们将此方法应用于新型儿童组合疫苗的安全性数据。

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