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本文引用的文献

1
Circadian and circaseptan rhythms in human epilepsy: a retrospective cohort study.人类癫痫的昼夜节律和近节律:一项回顾性队列研究。
Lancet Neurol. 2018 Nov;17(11):977-985. doi: 10.1016/S1474-4422(18)30274-6. Epub 2018 Sep 12.
2
Characteristics of large patient-reported outcomes: Where can one million seizures get us?大型患者报告结局的特征:一百万次癫痫发作能带给我们什么?
Epilepsia Open. 2018 Jul 4;3(3):364-373. doi: 10.1002/epi4.12237. eCollection 2018 Sep.
3
Seizure cluster: Definition, prevalence, consequences, and management.癫痫发作群:定义、发生率、后果和管理。
Seizure. 2019 May;68:9-15. doi: 10.1016/j.seizure.2018.05.013. Epub 2018 May 21.
4
Is seizure frequency variance a predictable quantity?癫痫发作频率的变化是一个可预测的量吗?
Ann Clin Transl Neurol. 2018 Jan 9;5(2):201-207. doi: 10.1002/acn3.519. eCollection 2018 Feb.
5
Multi-day rhythms modulate seizure risk in epilepsy.多日节律调节癫痫的发作风险。
Nat Commun. 2018 Jan 8;9(1):88. doi: 10.1038/s41467-017-02577-y.
6
A multi-dataset time-reversal approach to clinical trial placebo response and the relationship to natural variability in epilepsy.多数据集时间反转方法在临床试验安慰剂反应及与癫痫自然变异性的关系研究。
Seizure. 2017 Dec;53:31-36. doi: 10.1016/j.seizure.2017.10.016. Epub 2017 Oct 23.
7
Seizure self-prediction: Myth or missed opportunity?癫痫发作自我预测:是神话还是错失的机会?
Seizure. 2017 Oct;51:180-185. doi: 10.1016/j.seizure.2017.08.011. Epub 2017 Sep 1.
8
Monte Carlo simulations of randomized clinical trials in epilepsy.癫痫随机临床试验的蒙特卡罗模拟
Ann Clin Transl Neurol. 2017 May 24;4(8):544-552. doi: 10.1002/acn3.426. eCollection 2017 Aug.
9
A big data approach to the development of mixed-effects models for seizure count data.一种用于癫痫发作计数数据的混合效应模型开发的大数据方法。
Epilepsia. 2017 May;58(5):835-844. doi: 10.1111/epi.13727. Epub 2017 Mar 30.
10
Efficacy and safety of retigabine/ezogabine as adjunctive therapy in adult Asian patients with drug-resistant partial-onset seizures: A randomized, placebo-controlled Phase III study.瑞替加滨/依佐加滨作为成年亚洲耐药性部分性发作患者辅助治疗的疗效和安全性:一项随机、安慰剂对照的III期研究。
Epilepsy Behav. 2016 Aug;61:224-230. doi: 10.1016/j.yebeh.2016.05.018. Epub 2016 Jul 1.

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.

DOI:10.1016/j.eplepsyres.2020.106306
PMID:32172145
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7194486/
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。