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发作频率的自然变异性:对试验和安慰剂的影响。

Natural variability in seizure frequency: Implications for trials and placebo.

机构信息

Harvard Medical School Beth Israel Deaconess Medical Center, Department of Neurology, United States.

Harvard Medical School Beth Israel Deaconess Medical Center, Department of Neurology, United States.

出版信息

Epilepsy Res. 2020 May;162:106306. doi: 10.1016/j.eplepsyres.2020.106306. Epub 2020 Mar 6.

Abstract

BACKGROUND

Changes in patient-reported seizure frequencies are the gold standard used to test efficacy of new treatments in randomized controlled trials (RCTs). Recent analyses of patient seizure diary data suggest that the placebo response may be attributable to natural fluctuations in seizure frequency, though the evidence is incomplete. Here we develop a data-driven statistical model and assess the impact of the model on interpretation of placebo response.

METHODS

A synthetic seizure diary generator matching statistical properties seen across multiple epilepsy diary datasets was constructed. The model was used to simulate the placebo arm of 5000 RCTs. A meta-analysis of 23 historical RCTs was compared to the simulations.

RESULTS

The placebo 50 %-responder rate (RR50) was 27.3 ± 3.6 % (simulated) and 21.1 ± 10.0 % (historical). The placebo median percent change (MPC) was 22.0 ± 6.0 % (simulated) and 16.7 ± 10.3 % (historical).

CONCLUSIONS

A statistical model of daily seizure count generation which incorporates quantities related to the natural fluctuations of seizure count data produces a placebo response comparable to those seen in historical RCTs. This model may be useful in better understanding the seizure count fluctuations seen in patients in other clinical settings.

摘要

背景

患者报告的发作频率变化是测试随机对照试验(RCT)中新治疗方法疗效的金标准。最近对患者发作日记数据的分析表明,安慰剂反应可能归因于发作频率的自然波动,尽管证据并不完整。在这里,我们开发了一个数据驱动的统计模型,并评估了该模型对安慰剂反应解释的影响。

方法

构建了一个与多个癫痫日记数据集一致的统计特性的合成发作日记生成器。该模型用于模拟 5000 项 RCT 的安慰剂组。将 23 项历史 RCT 的荟萃分析与模拟进行了比较。

结果

安慰剂 50%缓解率(RR50)为 27.3±3.6%(模拟)和 21.1±10.0%(历史)。安慰剂中位数百分比变化(MPC)为 22.0±6.0%(模拟)和 16.7±10.3%(历史)。

结论

一种生成每日发作次数的统计模型,该模型包含与发作次数数据的自然波动相关的数量,可产生与历史 RCT 中观察到的安慰剂反应相当的反应。该模型可用于更好地理解其他临床环境中患者的发作次数波动。

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